Semantic integration to identify overlapping functional modules in protein interaction networks ↔ Ontology integration to identify protein complex in protein interaction networks

LATER¶0-4

PROCEEDINGS Open Access Ontology integration to identify protein complex in protein interaction networks Bo Xu*, Hongfei Lin, Zhihao Yang From International Workshop on Computational Proteomics Hong Kong, China. 18-21 December 2010 Abstract Background: Protein complexes can be identified from the protein interaction networks derived from experimental data sets. However, these analyses are challenging because of the presence of unreliable interactions and the complex connectivity of the network. The integration of protein-protein interactions with the data from other sources can be leveraged for improving the effectiveness of protein complexes detection algorithms. Methods: We have developed novel semantic similarity method, which use Gene Ontology (GO) annotations to measure the reliability of protein-protein interactions. The protein interaction networks can be converted into a weighted graph representation by assigning the reliability values to each interaction as a weight. Following the approach of that of the previously proposed clustering algorithm IPCA which expands clusters starting from seeded vertices, we present a clustering algorithm OIIP based on the new weighted Protein-Protein interaction networks for identifying protein complexes. Results: The algorithm OIIP is applied to the protein interaction network of Sacchromyces cerevisiae and identifies many well known complexes. Experimental results show that the algorithm OIIP has higher F-measure and accuracy compared to other competing approaches. Background In the post-genomic era, one of the most important issues is to systematically analyze and comprehensively understand the topology of biological networks and biochemical progress in cells. The current knowledge base of protein-protein interactions has been built from the heterogeneous data sources generated by high-throughput techniques [1-4].Protein complexes can help us to understand certain biological progress and to predict the functions of proteins. A wide range of graphtheoretic approaches have been employed for detecting protein complexes from protein interaction networks. However, they have been limited in accuracy due to the presence of unreliable interactions and the complex connectivity patterns of the networks. The experimental data sets are susceptible to false positives, i.e., some fraction of the

LATER¶0-4

PROCEEDINGS Open Access Ontology integration to identify protein complex in protein interaction networks Bo Xu*, Hongfei Lin, Zhihao Yang From International Workshop on Computational Proteomics Hong Kong, China. 18-21 December 2010 Abstract Background: Protein complexes can be identified from the protein interaction networks derived from experimental data sets. However, these analyses are challenging because of the presence of unreliable interactions and the complex connectivity of the network. The integration of protein-protein interactions with the data from other sources can be leveraged for improving the effectiveness of protein complexes detection algorithms. Methods: We have developed novel semantic similarity method, which use Gene Ontology (GO) annotations to measure the reliability of protein-protein interactions. The protein interaction networks can be converted into a weighted graph representation by assigning the reliability values to each interaction as a weight. Following the approach of that of the previously proposed clustering algorithm IPCA which expands clusters starting from seeded vertices, we present a clustering algorithm OIIP based on the new weighted Protein-Protein interaction networks for identifying protein complexes. Results: The algorithm OIIP is applied to the protein interaction network of Sacchromyces cerevisiae and identifies many well known complexes. Experimental results show that the algorithm OIIP has higher F-measure and accuracy compared to other competing approaches. Background In the post-genomic era, one of the most important issues is to systematically analyze and comprehensively understand the topology of biological networks and biochemical progress in cells. The current knowledge base of protein-protein interactions has been built from the heterogeneous data sources generated by high-throughput techniques [1-4].Protein complexes can help us to understand certain biological progress and to predict the functions of proteins. A wide range of graphtheoretic approaches have been employed for detecting protein complexes from protein interaction networks. However, they have been limited in accuracy due to the presence of unreliable interactions and the complex connectivity patterns of the networks. The experimental data sets are susceptible to false positives, i.e., some fraction of the

LATER¶0-4

PROCEEDINGS Open Access Ontology integration to identify protein complex in protein interaction networks Bo Xu*, Hongfei Lin, Zhihao Yang From International Workshop on Computational Proteomics Hong Kong, China. 18-21 December 2010 Abstract Background: Protein complexes can be identified from the protein interaction networks derived from experimental data sets. However, these analyses are challenging because of the presence of unreliable interactions and the complex connectivity of the network. The integration of protein-protein interactions with the data from other sources can be leveraged for improving the effectiveness of protein complexes detection algorithms. Methods: We have developed novel semantic similarity method, which use Gene Ontology (GO) annotations to measure the reliability of protein-protein interactions. The protein interaction networks can be converted into a weighted graph representation by assigning the reliability values to each interaction as a weight. Following the approach of that of the previously proposed clustering algorithm IPCA which expands clusters starting from seeded vertices, we present a clustering algorithm OIIP based on the new weighted Protein-Protein interaction networks for identifying protein complexes. Results: The algorithm OIIP is applied to the protein interaction network of Sacchromyces cerevisiae and identifies many well known complexes. Experimental results show that the algorithm OIIP has higher F-measure and accuracy compared to other competing approaches. Background In the post-genomic era, one of the most important issues is to systematically analyze and comprehensively understand the topology of biological networks and biochemical progress in cells. The current knowledge base of protein-protein interactions has been built from the heterogeneous data sources generated by high-throughput techniques [1-4].Protein complexes can help us to understand certain biological progress and to predict the functions of proteins. A wide range of graphtheoretic approaches have been employed for detecting protein complexes from protein interaction networks. However, they have been limited in accuracy due to the presence of unreliable interactions and the complex connectivity patterns of the networks. The experimental data sets are susceptible to false positives, i.e., some fraction of the

LATER¶2-6

Methods: We have developed novel semantic similarity method, which use Gene Ontology (GO) annotations to measure the reliability of protein-protein interactions. The protein interaction networks can be converted into a weighted graph representation by assigning the reliability values to each interaction as a weight. Following the approach of that of the previously proposed clustering algorithm IPCA which expands clusters starting from seeded vertices, we present a clustering algorithm OIIP based on the new weighted Protein-Protein interaction networks for identifying protein complexes. Results: The algorithm OIIP is applied to the protein interaction network of Sacchromyces cerevisiae and identifies many well known complexes. Experimental results show that the algorithm OIIP has higher F-measure and accuracy compared to other competing approaches. Background In the post-genomic era, one of the most important issues is to systematically analyze and comprehensively understand the topology of biological networks and biochemical progress in cells. The current knowledge base of protein-protein interactions has been built from the heterogeneous data sources generated by high-throughput techniques [1-4].Protein complexes can help us to understand certain biological progress and to predict the functions of proteins. A wide range of graphtheoretic approaches have been employed for detecting protein complexes from protein interaction networks. However, they have been limited in accuracy due to the presence of unreliable interactions and the complex connectivity patterns of the networks. The experimental data sets are susceptible to false positives, i.e., some fraction of the putative interactions detected must be considered spurious because they cannot be confirmed to occur in vivo [5]. To resolve the inaccuracy resulting from false connections, other functional knowledge can be integrated into the protein interaction networks. For example, many groups [6-8] have investigated the integration of gene expression data from microarray experiments to improve protein complexes identification. However, gene expression data are also susceptible to experimental sources of bias and noise. The correlations of mRNA levels with even cognate protein expression may be modest at best. These factors limit the usefulness of microarray data for assessing the reliability of proteinprotein interactions. Gene Ontology (GO) [9] is another useful data source to combine with the protein interaction networks. The GO is currently one of the most comprehensive and well-curated ontology databases in the bioinformatics community. It provides a collection of well-defined biological terms, called GO terms, * Correspondence: xubo@mail.dlut.edu.cn Department of Computer Science and Engineering, Dalian University of Technology, Dalian, China Full list of author information is available at the end of the article

LATER¶2-6

Methods: We have developed novel semantic similarity method, which use Gene Ontology (GO) annotations to measure the reliability of protein-protein interactions. The protein interaction networks can be converted into a weighted graph representation by assigning the reliability values to each interaction as a weight. Following the approach of that of the previously proposed clustering algorithm IPCA which expands clusters starting from seeded vertices, we present a clustering algorithm OIIP based on the new weighted Protein-Protein interaction networks for identifying protein complexes. Results: The algorithm OIIP is applied to the protein interaction network of Sacchromyces cerevisiae and identifies many well known complexes. Experimental results show that the algorithm OIIP has higher F-measure and accuracy compared to other competing approaches. Background In the post-genomic era, one of the most important issues is to systematically analyze and comprehensively understand the topology of biological networks and biochemical progress in cells. The current knowledge base of protein-protein interactions has been built from the heterogeneous data sources generated by high-throughput techniques [1-4].Protein complexes can help us to understand certain biological progress and to predict the functions of proteins. A wide range of graphtheoretic approaches have been employed for detecting protein complexes from protein interaction networks. However, they have been limited in accuracy due to the presence of unreliable interactions and the complex connectivity patterns of the networks. The experimental data sets are susceptible to false positives, i.e., some fraction of the putative interactions detected must be considered spurious because they cannot be confirmed to occur in vivo [5]. To resolve the inaccuracy resulting from false connections, other functional knowledge can be integrated into the protein interaction networks. For example, many groups [6-8] have investigated the integration of gene expression data from microarray experiments to improve protein complexes identification. However, gene expression data are also susceptible to experimental sources of bias and noise. The correlations of mRNA levels with even cognate protein expression may be modest at best. These factors limit the usefulness of microarray data for assessing the reliability of proteinprotein interactions. Gene Ontology (GO) [9] is another useful data source to combine with the protein interaction networks. The GO is currently one of the most comprehensive and well-curated ontology databases in the bioinformatics community. It provides a collection of well-defined biological terms, called GO terms, * Correspondence: xubo@mail.dlut.edu.cn Department of Computer Science and Engineering, Dalian University of Technology, Dalian, China Full list of author information is available at the end of the article

LATER¶2-7

Methods: We have developed novel semantic similarity method, which use Gene Ontology (GO) annotations to measure the reliability of protein-protein interactions. The protein interaction networks can be converted into a weighted graph representation by assigning the reliability values to each interaction as a weight. Following the approach of that of the previously proposed clustering algorithm IPCA which expands clusters starting from seeded vertices, we present a clustering algorithm OIIP based on the new weighted Protein-Protein interaction networks for identifying protein complexes. Results: The algorithm OIIP is applied to the protein interaction network of Sacchromyces cerevisiae and identifies many well known complexes. Experimental results show that the algorithm OIIP has higher F-measure and accuracy compared to other competing approaches. Background In the post-genomic era, one of the most important issues is to systematically analyze and comprehensively understand the topology of biological networks and biochemical progress in cells. The current knowledge base of protein-protein interactions has been built from the heterogeneous data sources generated by high-throughput techniques [1-4].Protein complexes can help us to understand certain biological progress and to predict the functions of proteins. A wide range of graphtheoretic approaches have been employed for detecting protein complexes from protein interaction networks. However, they have been limited in accuracy due to the presence of unreliable interactions and the complex connectivity patterns of the networks. The experimental data sets are susceptible to false positives, i.e., some fraction of the putative interactions detected must be considered spurious because they cannot be confirmed to occur in vivo [5]. To resolve the inaccuracy resulting from false connections, other functional knowledge can be integrated into the protein interaction networks. For example, many groups [6-8] have investigated the integration of gene expression data from microarray experiments to improve protein complexes identification. However, gene expression data are also susceptible to experimental sources of bias and noise. The correlations of mRNA levels with even cognate protein expression may be modest at best. These factors limit the usefulness of microarray data for assessing the reliability of proteinprotein interactions. Gene Ontology (GO) [9] is another useful data source to combine with the protein interaction networks. The GO is currently one of the most comprehensive and well-curated ontology databases in the bioinformatics community. It provides a collection of well-defined biological terms, called GO terms, * Correspondence: xubo@mail.dlut.edu.cn Department of Computer Science and Engineering, Dalian University of Technology, Dalian, China Full list of author information is available at the end of the article Xu et al. Proteome Science 2011, 9(Suppl 1):S7 http://www.proteomesci.com/content/9/S1/S7 © 2011 Xu et al; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

LATER¶3-7

Results: The algorithm OIIP is applied to the protein interaction network of Sacchromyces cerevisiae and identifies many well known complexes. Experimental results show that the algorithm OIIP has higher F-measure and accuracy compared to other competing approaches. Background In the post-genomic era, one of the most important issues is to systematically analyze and comprehensively understand the topology of biological networks and biochemical progress in cells. The current knowledge base of protein-protein interactions has been built from the heterogeneous data sources generated by high-throughput techniques [1-4].Protein complexes can help us to understand certain biological progress and to predict the functions of proteins. A wide range of graphtheoretic approaches have been employed for detecting protein complexes from protein interaction networks. However, they have been limited in accuracy due to the presence of unreliable interactions and the complex connectivity patterns of the networks. The experimental data sets are susceptible to false positives, i.e., some fraction of the putative interactions detected must be considered spurious because they cannot be confirmed to occur in vivo [5]. To resolve the inaccuracy resulting from false connections, other functional knowledge can be integrated into the protein interaction networks. For example, many groups [6-8] have investigated the integration of gene expression data from microarray experiments to improve protein complexes identification. However, gene expression data are also susceptible to experimental sources of bias and noise. The correlations of mRNA levels with even cognate protein expression may be modest at best. These factors limit the usefulness of microarray data for assessing the reliability of proteinprotein interactions. Gene Ontology (GO) [9] is another useful data source to combine with the protein interaction networks. The GO is currently one of the most comprehensive and well-curated ontology databases in the bioinformatics community. It provides a collection of well-defined biological terms, called GO terms, * Correspondence: xubo@mail.dlut.edu.cn Department of Computer Science and Engineering, Dalian University of Technology, Dalian, China Full list of author information is available at the end of the article Xu et al. Proteome Science 2011, 9(Suppl 1):S7 http://www.proteomesci.com/content/9/S1/S7 © 2011 Xu et al; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

LATER¶3-7

Results: The algorithm OIIP is applied to the protein interaction network of Sacchromyces cerevisiae and identifies many well known complexes. Experimental results show that the algorithm OIIP has higher F-measure and accuracy compared to other competing approaches. Background In the post-genomic era, one of the most important issues is to systematically analyze and comprehensively understand the topology of biological networks and biochemical progress in cells. The current knowledge base of protein-protein interactions has been built from the heterogeneous data sources generated by high-throughput techniques [1-4].Protein complexes can help us to understand certain biological progress and to predict the functions of proteins. A wide range of graphtheoretic approaches have been employed for detecting protein complexes from protein interaction networks. However, they have been limited in accuracy due to the presence of unreliable interactions and the complex connectivity patterns of the networks. The experimental data sets are susceptible to false positives, i.e., some fraction of the putative interactions detected must be considered spurious because they cannot be confirmed to occur in vivo [5]. To resolve the inaccuracy resulting from false connections, other functional knowledge can be integrated into the protein interaction networks. For example, many groups [6-8] have investigated the integration of gene expression data from microarray experiments to improve protein complexes identification. However, gene expression data are also susceptible to experimental sources of bias and noise. The correlations of mRNA levels with even cognate protein expression may be modest at best. These factors limit the usefulness of microarray data for assessing the reliability of proteinprotein interactions. Gene Ontology (GO) [9] is another useful data source to combine with the protein interaction networks. The GO is currently one of the most comprehensive and well-curated ontology databases in the bioinformatics community. It provides a collection of well-defined biological terms, called GO terms, * Correspondence: xubo@mail.dlut.edu.cn Department of Computer Science and Engineering, Dalian University of Technology, Dalian, China Full list of author information is available at the end of the article Xu et al. Proteome Science 2011, 9(Suppl 1):S7 http://www.proteomesci.com/content/9/S1/S7 © 2011 Xu et al; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

LATER¶5-9

putative interactions detected must be considered spurious because they cannot be confirmed to occur in vivo [5]. To resolve the inaccuracy resulting from false connections, other functional knowledge can be integrated into the protein interaction networks. For example, many groups [6-8] have investigated the integration of gene expression data from microarray experiments to improve protein complexes identification. However, gene expression data are also susceptible to experimental sources of bias and noise. The correlations of mRNA levels with even cognate protein expression may be modest at best. These factors limit the usefulness of microarray data for assessing the reliability of proteinprotein interactions. Gene Ontology (GO) [9] is another useful data source to combine with the protein interaction networks. The GO is currently one of the most comprehensive and well-curated ontology databases in the bioinformatics community. It provides a collection of well-defined biological terms, called GO terms, * Correspondence: xubo@mail.dlut.edu.cn Department of Computer Science and Engineering, Dalian University of Technology, Dalian, China Full list of author information is available at the end of the article Xu et al. Proteome Science 2011, 9(Suppl 1):S7 http://www.proteomesci.com/content/9/S1/S7 © 2011 Xu et al; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. spanning biological processes, molecular functions and cellular components. The GO has been used to facilitate the analysis of gene expression data [10-12]. In this work, we integrate protein-protein interactions with the information content in the GO annotation database and topology weights to enhance the modularization of interaction networks. An unweighted protein interaction network can be converted into a weighted graph representation by assigning a weight to each interaction [13]. The weight of each interaction is interpreted as its reliability, i.e., the probability of the interaction being a true positive. We propose a novel method to measure the reliability of protein-protein interactions using GO annotation data and topology weights. Following the approach of that of the previously proposed clustering algorithm IPCA[14] which expands clusters starting from seeded vertices, we present a clustering algorithm OIIP based on the new large weighted protein interaction networks.

LATER¶6-10

* Correspondence: xubo@mail.dlut.edu.cn Department of Computer Science and Engineering, Dalian University of Technology, Dalian, China Full list of author information is available at the end of the article Xu et al. Proteome Science 2011, 9(Suppl 1):S7 http://www.proteomesci.com/content/9/S1/S7 © 2011 Xu et al; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. spanning biological processes, molecular functions and cellular components. The GO has been used to facilitate the analysis of gene expression data [10-12]. In this work, we integrate protein-protein interactions with the information content in the GO annotation database and topology weights to enhance the modularization of interaction networks. An unweighted protein interaction network can be converted into a weighted graph representation by assigning a weight to each interaction [13]. The weight of each interaction is interpreted as its reliability, i.e., the probability of the interaction being a true positive. We propose a novel method to measure the reliability of protein-protein interactions using GO annotation data and topology weights. Following the approach of that of the previously proposed clustering algorithm IPCA[14] which expands clusters starting from seeded vertices, we present a clustering algorithm OIIP based on the new large weighted protein interaction networks. Methods Weighted network Weights quantify the likelihood of the interaction between every pair of proteins, and they can be estimated by encoding the proteins using gene ontology (GO) consortium. “Ontology” is a specification of a conceptualization that refers to the subject of existence. GO is established by the following three criteria: (I) biological process referring to a biological objective to which the gene or gene product contributes; (II) molecular function defined as the biochemical activity of a gene product; (III) cellular component referring to the place in the cell where a gene product is active. It is very common for the same protein or proteins in the same subfamily to form protein complexes, for example, protein Ste2p and Ste3p from a complex that is among activated G protein-coupled receptors in yeast cellular mating.[15] It is also common for proteins in heterofamilies to form protein complexes if they share a conservative motif, for example, protein Ctf19, Mcm21, and Okp1 from a heterocomplex in the budding yeast kinetochore.[16] Complicated protein complexes may be formed by multiple proteins, some of which share same biological processes and some are from the same subfamily, for example, Dsl1p complex, involved in GolgiER retrograde transport, includes Dsl1p, Dsl3p, Q/tSNARE proteins, and so forth.[17] Thus GO consortium is considered to be a very helpful vehicle for investigating protein-protein interactions,[18] because these three criteria reflect the attribute of gene, gene product, geneproduct groups and the subcellular localization[19-21].

LATER¶7-11

Xu et al. Proteome Science 2011, 9(Suppl 1):S7 http://www.proteomesci.com/content/9/S1/S7 © 2011 Xu et al; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. spanning biological processes, molecular functions and cellular components. The GO has been used to facilitate the analysis of gene expression data [10-12]. In this work, we integrate protein-protein interactions with the information content in the GO annotation database and topology weights to enhance the modularization of interaction networks. An unweighted protein interaction network can be converted into a weighted graph representation by assigning a weight to each interaction [13]. The weight of each interaction is interpreted as its reliability, i.e., the probability of the interaction being a true positive. We propose a novel method to measure the reliability of protein-protein interactions using GO annotation data and topology weights. Following the approach of that of the previously proposed clustering algorithm IPCA[14] which expands clusters starting from seeded vertices, we present a clustering algorithm OIIP based on the new large weighted protein interaction networks. Methods Weighted network Weights quantify the likelihood of the interaction between every pair of proteins, and they can be estimated by encoding the proteins using gene ontology (GO) consortium. “Ontology” is a specification of a conceptualization that refers to the subject of existence. GO is established by the following three criteria: (I) biological process referring to a biological objective to which the gene or gene product contributes; (II) molecular function defined as the biochemical activity of a gene product; (III) cellular component referring to the place in the cell where a gene product is active. It is very common for the same protein or proteins in the same subfamily to form protein complexes, for example, protein Ste2p and Ste3p from a complex that is among activated G protein-coupled receptors in yeast cellular mating.[15] It is also common for proteins in heterofamilies to form protein complexes if they share a conservative motif, for example, protein Ctf19, Mcm21, and Okp1 from a heterocomplex in the budding yeast kinetochore.[16] Complicated protein complexes may be formed by multiple proteins, some of which share same biological processes and some are from the same subfamily, for example, Dsl1p complex, involved in GolgiER retrograde transport, includes Dsl1p, Dsl3p, Q/tSNARE proteins, and so forth.[17] Thus GO consortium is considered to be a very helpful vehicle for investigating protein-protein interactions,[18] because these three criteria reflect the attribute of gene, gene product, geneproduct groups and the subcellular localization[19-21]. interactions to estimate the similarity between two proteins. Based on the previous method [23], we proposed our semantic similarity method. We define an annotation size of a GO term as the number of annotated proteins on the GO term. The semantic similarity between two proteins is then calculated based on the annotation size of the GO term, on which both proteins are annotated. According to the transitivity property of GO annotation, if a protein x is annotated on a GO term gi, it is also annotated on the GO terms on the path from gi to the root GO term in the GO structure. Thus, the proportion of the annotation size of a GO term to the total number of annotated proteins can quantify the specificity of the GO term. If two proteins are annotated on a more specific GO term and have more common GO terms, then they are functionally more similar.

LATER¶7-11

Xu et al. Proteome Science 2011, 9(Suppl 1):S7 http://www.proteomesci.com/content/9/S1/S7 © 2011 Xu et al; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. spanning biological processes, molecular functions and cellular components. The GO has been used to facilitate the analysis of gene expression data [10-12]. In this work, we integrate protein-protein interactions with the information content in the GO annotation database and topology weights to enhance the modularization of interaction networks. An unweighted protein interaction network can be converted into a weighted graph representation by assigning a weight to each interaction [13]. The weight of each interaction is interpreted as its reliability, i.e., the probability of the interaction being a true positive. We propose a novel method to measure the reliability of protein-protein interactions using GO annotation data and topology weights. Following the approach of that of the previously proposed clustering algorithm IPCA[14] which expands clusters starting from seeded vertices, we present a clustering algorithm OIIP based on the new large weighted protein interaction networks. Methods Weighted network Weights quantify the likelihood of the interaction between every pair of proteins, and they can be estimated by encoding the proteins using gene ontology (GO) consortium. “Ontology” is a specification of a conceptualization that refers to the subject of existence. GO is established by the following three criteria: (I) biological process referring to a biological objective to which the gene or gene product contributes; (II) molecular function defined as the biochemical activity of a gene product; (III) cellular component referring to the place in the cell where a gene product is active. It is very common for the same protein or proteins in the same subfamily to form protein complexes, for example, protein Ste2p and Ste3p from a complex that is among activated G protein-coupled receptors in yeast cellular mating.[15] It is also common for proteins in heterofamilies to form protein complexes if they share a conservative motif, for example, protein Ctf19, Mcm21, and Okp1 from a heterocomplex in the budding yeast kinetochore.[16] Complicated protein complexes may be formed by multiple proteins, some of which share same biological processes and some are from the same subfamily, for example, Dsl1p complex, involved in GolgiER retrograde transport, includes Dsl1p, Dsl3p, Q/tSNARE proteins, and so forth.[17] Thus GO consortium is considered to be a very helpful vehicle for investigating protein-protein interactions,[18] because these three criteria reflect the attribute of gene, gene product, geneproduct groups and the subcellular localization[19-21]. interactions to estimate the similarity between two proteins. Based on the previous method [23], we proposed our semantic similarity method. We define an annotation size of a GO term as the number of annotated proteins on the GO term. The semantic similarity between two proteins is then calculated based on the annotation size of the GO term, on which both proteins are annotated. According to the transitivity property of GO annotation, if a protein x is annotated on a GO term gi, it is also annotated on the GO terms on the path from gi to the root GO term in the GO structure. Thus, the proportion of the annotation size of a GO term to the total number of annotated proteins can quantify the specificity of the GO term. If two proteins are annotated on a more specific GO term and have more common GO terms, then they are functionally more similar.

LATER¶7-11

Xu et al. Proteome Science 2011, 9(Suppl 1):S7 http://www.proteomesci.com/content/9/S1/S7 © 2011 Xu et al; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. spanning biological processes, molecular functions and cellular components. The GO has been used to facilitate the analysis of gene expression data [10-12]. In this work, we integrate protein-protein interactions with the information content in the GO annotation database and topology weights to enhance the modularization of interaction networks. An unweighted protein interaction network can be converted into a weighted graph representation by assigning a weight to each interaction [13]. The weight of each interaction is interpreted as its reliability, i.e., the probability of the interaction being a true positive. We propose a novel method to measure the reliability of protein-protein interactions using GO annotation data and topology weights. Following the approach of that of the previously proposed clustering algorithm IPCA[14] which expands clusters starting from seeded vertices, we present a clustering algorithm OIIP based on the new large weighted protein interaction networks. Methods Weighted network Weights quantify the likelihood of the interaction between every pair of proteins, and they can be estimated by encoding the proteins using gene ontology (GO) consortium. “Ontology” is a specification of a conceptualization that refers to the subject of existence. GO is established by the following three criteria: (I) biological process referring to a biological objective to which the gene or gene product contributes; (II) molecular function defined as the biochemical activity of a gene product; (III) cellular component referring to the place in the cell where a gene product is active. It is very common for the same protein or proteins in the same subfamily to form protein complexes, for example, protein Ste2p and Ste3p from a complex that is among activated G protein-coupled receptors in yeast cellular mating.[15] It is also common for proteins in heterofamilies to form protein complexes if they share a conservative motif, for example, protein Ctf19, Mcm21, and Okp1 from a heterocomplex in the budding yeast kinetochore.[16] Complicated protein complexes may be formed by multiple proteins, some of which share same biological processes and some are from the same subfamily, for example, Dsl1p complex, involved in GolgiER retrograde transport, includes Dsl1p, Dsl3p, Q/tSNARE proteins, and so forth.[17] Thus GO consortium is considered to be a very helpful vehicle for investigating protein-protein interactions,[18] because these three criteria reflect the attribute of gene, gene product, geneproduct groups and the subcellular localization[19-21]. interactions to estimate the similarity between two proteins. Based on the previous method [23], we proposed our semantic similarity method. We define an annotation size of a GO term as the number of annotated proteins on the GO term. The semantic similarity between two proteins is then calculated based on the annotation size of the GO term, on which both proteins are annotated. According to the transitivity property of GO annotation, if a protein x is annotated on a GO term gi, it is also annotated on the GO terms on the path from gi to the root GO term in the GO structure. Thus, the proportion of the annotation size of a GO term to the total number of annotated proteins can quantify the specificity of the GO term. If two proteins are annotated on a more specific GO term and have more common GO terms, then they are functionally more similar.

LATER¶9-13

In this work, we integrate protein-protein interactions with the information content in the GO annotation database and topology weights to enhance the modularization of interaction networks. An unweighted protein interaction network can be converted into a weighted graph representation by assigning a weight to each interaction [13]. The weight of each interaction is interpreted as its reliability, i.e., the probability of the interaction being a true positive. We propose a novel method to measure the reliability of protein-protein interactions using GO annotation data and topology weights. Following the approach of that of the previously proposed clustering algorithm IPCA[14] which expands clusters starting from seeded vertices, we present a clustering algorithm OIIP based on the new large weighted protein interaction networks. Methods Weighted network Weights quantify the likelihood of the interaction between every pair of proteins, and they can be estimated by encoding the proteins using gene ontology (GO) consortium. “Ontology” is a specification of a conceptualization that refers to the subject of existence. GO is established by the following three criteria: (I) biological process referring to a biological objective to which the gene or gene product contributes; (II) molecular function defined as the biochemical activity of a gene product; (III) cellular component referring to the place in the cell where a gene product is active. It is very common for the same protein or proteins in the same subfamily to form protein complexes, for example, protein Ste2p and Ste3p from a complex that is among activated G protein-coupled receptors in yeast cellular mating.[15] It is also common for proteins in heterofamilies to form protein complexes if they share a conservative motif, for example, protein Ctf19, Mcm21, and Okp1 from a heterocomplex in the budding yeast kinetochore.[16] Complicated protein complexes may be formed by multiple proteins, some of which share same biological processes and some are from the same subfamily, for example, Dsl1p complex, involved in GolgiER retrograde transport, includes Dsl1p, Dsl3p, Q/tSNARE proteins, and so forth.[17] Thus GO consortium is considered to be a very helpful vehicle for investigating protein-protein interactions,[18] because these three criteria reflect the attribute of gene, gene product, geneproduct groups and the subcellular localization[19-21]. interactions to estimate the similarity between two proteins. Based on the previous method [23], we proposed our semantic similarity method. We define an annotation size of a GO term as the number of annotated proteins on the GO term. The semantic similarity between two proteins is then calculated based on the annotation size of the GO term, on which both proteins are annotated. According to the transitivity property of GO annotation, if a protein x is annotated on a GO term gi, it is also annotated on the GO terms on the path from gi to the root GO term in the GO structure. Thus, the proportion of the annotation size of a GO term to the total number of annotated proteins can quantify the specificity of the GO term. If two proteins are annotated on a more specific GO term and have more common GO terms, then they are functionally more similar. Suppose a protein x is annotated on m different GO terms. Si(x) denotes a set of annotated proteins on the GO term gi, whose annotation includes x, where 1≤i≤m. In the same way, suppose both x and y are annotated on n different GO terms, where n≤m. Sj(x, y) denotes a set of annotated proteins on the GO term gj, whose annotation includes x and y, where 1≤j≤n. Then, the minimum size of Si(x), mini|Si(x)|, is less than or equal to minj|Sj(x, y)|.C(x,y) denotes the sets of GO terms, whose annotation includes x and y. |C(x,y)| is the number of common GO terms which x and y both have. Suppose the size of annotation represents the number of annotated proteins on a GO term. Using the annotation size of the most specific GO term, on which two proteins x and y are annotated, we define semantic similarity Ssem(x, y) between x and y as follows:

LATER¶10-14

Methods Weighted network Weights quantify the likelihood of the interaction between every pair of proteins, and they can be estimated by encoding the proteins using gene ontology (GO) consortium. “Ontology” is a specification of a conceptualization that refers to the subject of existence. GO is established by the following three criteria: (I) biological process referring to a biological objective to which the gene or gene product contributes; (II) molecular function defined as the biochemical activity of a gene product; (III) cellular component referring to the place in the cell where a gene product is active. It is very common for the same protein or proteins in the same subfamily to form protein complexes, for example, protein Ste2p and Ste3p from a complex that is among activated G protein-coupled receptors in yeast cellular mating.[15] It is also common for proteins in heterofamilies to form protein complexes if they share a conservative motif, for example, protein Ctf19, Mcm21, and Okp1 from a heterocomplex in the budding yeast kinetochore.[16] Complicated protein complexes may be formed by multiple proteins, some of which share same biological processes and some are from the same subfamily, for example, Dsl1p complex, involved in GolgiER retrograde transport, includes Dsl1p, Dsl3p, Q/tSNARE proteins, and so forth.[17] Thus GO consortium is considered to be a very helpful vehicle for investigating protein-protein interactions,[18] because these three criteria reflect the attribute of gene, gene product, geneproduct groups and the subcellular localization[19-21]. interactions to estimate the similarity between two proteins. Based on the previous method [23], we proposed our semantic similarity method. We define an annotation size of a GO term as the number of annotated proteins on the GO term. The semantic similarity between two proteins is then calculated based on the annotation size of the GO term, on which both proteins are annotated. According to the transitivity property of GO annotation, if a protein x is annotated on a GO term gi, it is also annotated on the GO terms on the path from gi to the root GO term in the GO structure. Thus, the proportion of the annotation size of a GO term to the total number of annotated proteins can quantify the specificity of the GO term. If two proteins are annotated on a more specific GO term and have more common GO terms, then they are functionally more similar. Suppose a protein x is annotated on m different GO terms. Si(x) denotes a set of annotated proteins on the GO term gi, whose annotation includes x, where 1≤i≤m. In the same way, suppose both x and y are annotated on n different GO terms, where n≤m. Sj(x, y) denotes a set of annotated proteins on the GO term gj, whose annotation includes x and y, where 1≤j≤n. Then, the minimum size of Si(x), mini|Si(x)|, is less than or equal to minj|Sj(x, y)|.C(x,y) denotes the sets of GO terms, whose annotation includes x and y. |C(x,y)| is the number of common GO terms which x and y both have. Suppose the size of annotation represents the number of annotated proteins on a GO term. Using the annotation size of the most specific GO term, on which two proteins x and y are annotated, we define semantic similarity Ssem(x, y) between x and y as follows: S x y C x y S x y S sem j j ( , ) | ( , )| log(min | ( , )| | | ) max = − × (1) Smax is the maximum size of annotation among all GO terms in a DAG structure. If two proteins x and y are annotated on a more specific GO term and more common GO terms than x and z, then x is semantically more similar to y than z.

LATER¶10-14

Methods Weighted network Weights quantify the likelihood of the interaction between every pair of proteins, and they can be estimated by encoding the proteins using gene ontology (GO) consortium. “Ontology” is a specification of a conceptualization that refers to the subject of existence. GO is established by the following three criteria: (I) biological process referring to a biological objective to which the gene or gene product contributes; (II) molecular function defined as the biochemical activity of a gene product; (III) cellular component referring to the place in the cell where a gene product is active. It is very common for the same protein or proteins in the same subfamily to form protein complexes, for example, protein Ste2p and Ste3p from a complex that is among activated G protein-coupled receptors in yeast cellular mating.[15] It is also common for proteins in heterofamilies to form protein complexes if they share a conservative motif, for example, protein Ctf19, Mcm21, and Okp1 from a heterocomplex in the budding yeast kinetochore.[16] Complicated protein complexes may be formed by multiple proteins, some of which share same biological processes and some are from the same subfamily, for example, Dsl1p complex, involved in GolgiER retrograde transport, includes Dsl1p, Dsl3p, Q/tSNARE proteins, and so forth.[17] Thus GO consortium is considered to be a very helpful vehicle for investigating protein-protein interactions,[18] because these three criteria reflect the attribute of gene, gene product, geneproduct groups and the subcellular localization[19-21]. interactions to estimate the similarity between two proteins. Based on the previous method [23], we proposed our semantic similarity method. We define an annotation size of a GO term as the number of annotated proteins on the GO term. The semantic similarity between two proteins is then calculated based on the annotation size of the GO term, on which both proteins are annotated. According to the transitivity property of GO annotation, if a protein x is annotated on a GO term gi, it is also annotated on the GO terms on the path from gi to the root GO term in the GO structure. Thus, the proportion of the annotation size of a GO term to the total number of annotated proteins can quantify the specificity of the GO term. If two proteins are annotated on a more specific GO term and have more common GO terms, then they are functionally more similar. Suppose a protein x is annotated on m different GO terms. Si(x) denotes a set of annotated proteins on the GO term gi, whose annotation includes x, where 1≤i≤m. In the same way, suppose both x and y are annotated on n different GO terms, where n≤m. Sj(x, y) denotes a set of annotated proteins on the GO term gj, whose annotation includes x and y, where 1≤j≤n. Then, the minimum size of Si(x), mini|Si(x)|, is less than or equal to minj|Sj(x, y)|.C(x,y) denotes the sets of GO terms, whose annotation includes x and y. |C(x,y)| is the number of common GO terms which x and y both have. Suppose the size of annotation represents the number of annotated proteins on a GO term. Using the annotation size of the most specific GO term, on which two proteins x and y are annotated, we define semantic similarity Ssem(x, y) between x and y as follows: S x y C x y S x y S sem j j ( , ) | ( , )| log(min | ( , )| | | ) max = − × (1) Smax is the maximum size of annotation among all GO terms in a DAG structure. If two proteins x and y are annotated on a more specific GO term and more common GO terms than x and z, then x is semantically more similar to y than z.

LATER¶10-14

Methods Weighted network Weights quantify the likelihood of the interaction between every pair of proteins, and they can be estimated by encoding the proteins using gene ontology (GO) consortium. “Ontology” is a specification of a conceptualization that refers to the subject of existence. GO is established by the following three criteria: (I) biological process referring to a biological objective to which the gene or gene product contributes; (II) molecular function defined as the biochemical activity of a gene product; (III) cellular component referring to the place in the cell where a gene product is active. It is very common for the same protein or proteins in the same subfamily to form protein complexes, for example, protein Ste2p and Ste3p from a complex that is among activated G protein-coupled receptors in yeast cellular mating.[15] It is also common for proteins in heterofamilies to form protein complexes if they share a conservative motif, for example, protein Ctf19, Mcm21, and Okp1 from a heterocomplex in the budding yeast kinetochore.[16] Complicated protein complexes may be formed by multiple proteins, some of which share same biological processes and some are from the same subfamily, for example, Dsl1p complex, involved in GolgiER retrograde transport, includes Dsl1p, Dsl3p, Q/tSNARE proteins, and so forth.[17] Thus GO consortium is considered to be a very helpful vehicle for investigating protein-protein interactions,[18] because these three criteria reflect the attribute of gene, gene product, geneproduct groups and the subcellular localization[19-21]. interactions to estimate the similarity between two proteins. Based on the previous method [23], we proposed our semantic similarity method. We define an annotation size of a GO term as the number of annotated proteins on the GO term. The semantic similarity between two proteins is then calculated based on the annotation size of the GO term, on which both proteins are annotated. According to the transitivity property of GO annotation, if a protein x is annotated on a GO term gi, it is also annotated on the GO terms on the path from gi to the root GO term in the GO structure. Thus, the proportion of the annotation size of a GO term to the total number of annotated proteins can quantify the specificity of the GO term. If two proteins are annotated on a more specific GO term and have more common GO terms, then they are functionally more similar. Suppose a protein x is annotated on m different GO terms. Si(x) denotes a set of annotated proteins on the GO term gi, whose annotation includes x, where 1≤i≤m. In the same way, suppose both x and y are annotated on n different GO terms, where n≤m. Sj(x, y) denotes a set of annotated proteins on the GO term gj, whose annotation includes x and y, where 1≤j≤n. Then, the minimum size of Si(x), mini|Si(x)|, is less than or equal to minj|Sj(x, y)|.C(x,y) denotes the sets of GO terms, whose annotation includes x and y. |C(x,y)| is the number of common GO terms which x and y both have. Suppose the size of annotation represents the number of annotated proteins on a GO term. Using the annotation size of the most specific GO term, on which two proteins x and y are annotated, we define semantic similarity Ssem(x, y) between x and y as follows: S x y C x y S x y S sem j j ( , ) | ( , )| log(min | ( , )| | | ) max = − × (1) Smax is the maximum size of annotation among all GO terms in a DAG structure. If two proteins x and y are annotated on a more specific GO term and more common GO terms than x and z, then x is semantically more similar to y than z.

LATER¶10-14

Methods Weighted network Weights quantify the likelihood of the interaction between every pair of proteins, and they can be estimated by encoding the proteins using gene ontology (GO) consortium. “Ontology” is a specification of a conceptualization that refers to the subject of existence. GO is established by the following three criteria: (I) biological process referring to a biological objective to which the gene or gene product contributes; (II) molecular function defined as the biochemical activity of a gene product; (III) cellular component referring to the place in the cell where a gene product is active. It is very common for the same protein or proteins in the same subfamily to form protein complexes, for example, protein Ste2p and Ste3p from a complex that is among activated G protein-coupled receptors in yeast cellular mating.[15] It is also common for proteins in heterofamilies to form protein complexes if they share a conservative motif, for example, protein Ctf19, Mcm21, and Okp1 from a heterocomplex in the budding yeast kinetochore.[16] Complicated protein complexes may be formed by multiple proteins, some of which share same biological processes and some are from the same subfamily, for example, Dsl1p complex, involved in GolgiER retrograde transport, includes Dsl1p, Dsl3p, Q/tSNARE proteins, and so forth.[17] Thus GO consortium is considered to be a very helpful vehicle for investigating protein-protein interactions,[18] because these three criteria reflect the attribute of gene, gene product, geneproduct groups and the subcellular localization[19-21]. interactions to estimate the similarity between two proteins. Based on the previous method [23], we proposed our semantic similarity method. We define an annotation size of a GO term as the number of annotated proteins on the GO term. The semantic similarity between two proteins is then calculated based on the annotation size of the GO term, on which both proteins are annotated. According to the transitivity property of GO annotation, if a protein x is annotated on a GO term gi, it is also annotated on the GO terms on the path from gi to the root GO term in the GO structure. Thus, the proportion of the annotation size of a GO term to the total number of annotated proteins can quantify the specificity of the GO term. If two proteins are annotated on a more specific GO term and have more common GO terms, then they are functionally more similar. Suppose a protein x is annotated on m different GO terms. Si(x) denotes a set of annotated proteins on the GO term gi, whose annotation includes x, where 1≤i≤m. In the same way, suppose both x and y are annotated on n different GO terms, where n≤m. Sj(x, y) denotes a set of annotated proteins on the GO term gj, whose annotation includes x and y, where 1≤j≤n. Then, the minimum size of Si(x), mini|Si(x)|, is less than or equal to minj|Sj(x, y)|.C(x,y) denotes the sets of GO terms, whose annotation includes x and y. |C(x,y)| is the number of common GO terms which x and y both have. Suppose the size of annotation represents the number of annotated proteins on a GO term. Using the annotation size of the most specific GO term, on which two proteins x and y are annotated, we define semantic similarity Ssem(x, y) between x and y as follows: S x y C x y S x y S sem j j ( , ) | ( , )| log(min | ( , )| | | ) max = − × (1) Smax is the maximum size of annotation among all GO terms in a DAG structure. If two proteins x and y are annotated on a more specific GO term and more common GO terms than x and z, then x is semantically more similar to y than z.

LATER¶10-14

Methods Weighted network Weights quantify the likelihood of the interaction between every pair of proteins, and they can be estimated by encoding the proteins using gene ontology (GO) consortium. “Ontology” is a specification of a conceptualization that refers to the subject of existence. GO is established by the following three criteria: (I) biological process referring to a biological objective to which the gene or gene product contributes; (II) molecular function defined as the biochemical activity of a gene product; (III) cellular component referring to the place in the cell where a gene product is active. It is very common for the same protein or proteins in the same subfamily to form protein complexes, for example, protein Ste2p and Ste3p from a complex that is among activated G protein-coupled receptors in yeast cellular mating.[15] It is also common for proteins in heterofamilies to form protein complexes if they share a conservative motif, for example, protein Ctf19, Mcm21, and Okp1 from a heterocomplex in the budding yeast kinetochore.[16] Complicated protein complexes may be formed by multiple proteins, some of which share same biological processes and some are from the same subfamily, for example, Dsl1p complex, involved in GolgiER retrograde transport, includes Dsl1p, Dsl3p, Q/tSNARE proteins, and so forth.[17] Thus GO consortium is considered to be a very helpful vehicle for investigating protein-protein interactions,[18] because these three criteria reflect the attribute of gene, gene product, geneproduct groups and the subcellular localization[19-21]. interactions to estimate the similarity between two proteins. Based on the previous method [23], we proposed our semantic similarity method. We define an annotation size of a GO term as the number of annotated proteins on the GO term. The semantic similarity between two proteins is then calculated based on the annotation size of the GO term, on which both proteins are annotated. According to the transitivity property of GO annotation, if a protein x is annotated on a GO term gi, it is also annotated on the GO terms on the path from gi to the root GO term in the GO structure. Thus, the proportion of the annotation size of a GO term to the total number of annotated proteins can quantify the specificity of the GO term. If two proteins are annotated on a more specific GO term and have more common GO terms, then they are functionally more similar. Suppose a protein x is annotated on m different GO terms. Si(x) denotes a set of annotated proteins on the GO term gi, whose annotation includes x, where 1≤i≤m. In the same way, suppose both x and y are annotated on n different GO terms, where n≤m. Sj(x, y) denotes a set of annotated proteins on the GO term gj, whose annotation includes x and y, where 1≤j≤n. Then, the minimum size of Si(x), mini|Si(x)|, is less than or equal to minj|Sj(x, y)|.C(x,y) denotes the sets of GO terms, whose annotation includes x and y. |C(x,y)| is the number of common GO terms which x and y both have. Suppose the size of annotation represents the number of annotated proteins on a GO term. Using the annotation size of the most specific GO term, on which two proteins x and y are annotated, we define semantic similarity Ssem(x, y) between x and y as follows: S x y C x y S x y S sem j j ( , ) | ( , )| log(min | ( , )| | | ) max = − × (1) Smax is the maximum size of annotation among all GO terms in a DAG structure. If two proteins x and y are annotated on a more specific GO term and more common GO terms than x and z, then x is semantically more similar to y than z.

LATER¶11-15

interactions to estimate the similarity between two proteins. Based on the previous method [23], we proposed our semantic similarity method. We define an annotation size of a GO term as the number of annotated proteins on the GO term. The semantic similarity between two proteins is then calculated based on the annotation size of the GO term, on which both proteins are annotated. According to the transitivity property of GO annotation, if a protein x is annotated on a GO term gi, it is also annotated on the GO terms on the path from gi to the root GO term in the GO structure. Thus, the proportion of the annotation size of a GO term to the total number of annotated proteins can quantify the specificity of the GO term. If two proteins are annotated on a more specific GO term and have more common GO terms, then they are functionally more similar. Suppose a protein x is annotated on m different GO terms. Si(x) denotes a set of annotated proteins on the GO term gi, whose annotation includes x, where 1≤i≤m. In the same way, suppose both x and y are annotated on n different GO terms, where n≤m. Sj(x, y) denotes a set of annotated proteins on the GO term gj, whose annotation includes x and y, where 1≤j≤n. Then, the minimum size of Si(x), mini|Si(x)|, is less than or equal to minj|Sj(x, y)|.C(x,y) denotes the sets of GO terms, whose annotation includes x and y. |C(x,y)| is the number of common GO terms which x and y both have. Suppose the size of annotation represents the number of annotated proteins on a GO term. Using the annotation size of the most specific GO term, on which two proteins x and y are annotated, we define semantic similarity Ssem(x, y) between x and y as follows: S x y C x y S x y S sem j j ( , ) | ( , )| log(min | ( , )| | | ) max = − × (1) Smax is the maximum size of annotation among all GO terms in a DAG structure. If two proteins x and y are annotated on a more specific GO term and more common GO terms than x and z, then x is semantically more similar to y than z. Considering the graph topology, we also involve the topology weight. For an input graph G = (V, E), we assign the topology weight of an edge [u, v] to be the number of neighbors shared by the vertices u and v. Then we assigned the sum of Ssem(u, v) and topology weight to the edge between u and v as a weight.

LATER¶12-16

Suppose a protein x is annotated on m different GO terms. Si(x) denotes a set of annotated proteins on the GO term gi, whose annotation includes x, where 1≤i≤m. In the same way, suppose both x and y are annotated on n different GO terms, where n≤m. Sj(x, y) denotes a set of annotated proteins on the GO term gj, whose annotation includes x and y, where 1≤j≤n. Then, the minimum size of Si(x), mini|Si(x)|, is less than or equal to minj|Sj(x, y)|.C(x,y) denotes the sets of GO terms, whose annotation includes x and y. |C(x,y)| is the number of common GO terms which x and y both have. Suppose the size of annotation represents the number of annotated proteins on a GO term. Using the annotation size of the most specific GO term, on which two proteins x and y are annotated, we define semantic similarity Ssem(x, y) between x and y as follows: S x y C x y S x y S sem j j ( , ) | ( , )| log(min | ( , )| | | ) max = − × (1) Smax is the maximum size of annotation among all GO terms in a DAG structure. If two proteins x and y are annotated on a more specific GO term and more common GO terms than x and z, then x is semantically more similar to y than z. Considering the graph topology, we also involve the topology weight. For an input graph G = (V, E), we assign the topology weight of an edge [u, v] to be the number of neighbors shared by the vertices u and v. Then we assigned the sum of Ssem(u, v) and topology weight to the edge between u and v as a weight. Weighted vertex and selecting seed We define the weight of each vertex to be the sum of the weights of its incident edges. After all vertices are assigned weights, we also sort in non-increasing order the vertices by their weights and store them in a queue Sq (vertices of the same weight are ordered in terms of their degrees).

LATER¶12-16

Suppose a protein x is annotated on m different GO terms. Si(x) denotes a set of annotated proteins on the GO term gi, whose annotation includes x, where 1≤i≤m. In the same way, suppose both x and y are annotated on n different GO terms, where n≤m. Sj(x, y) denotes a set of annotated proteins on the GO term gj, whose annotation includes x and y, where 1≤j≤n. Then, the minimum size of Si(x), mini|Si(x)|, is less than or equal to minj|Sj(x, y)|.C(x,y) denotes the sets of GO terms, whose annotation includes x and y. |C(x,y)| is the number of common GO terms which x and y both have. Suppose the size of annotation represents the number of annotated proteins on a GO term. Using the annotation size of the most specific GO term, on which two proteins x and y are annotated, we define semantic similarity Ssem(x, y) between x and y as follows: S x y C x y S x y S sem j j ( , ) | ( , )| log(min | ( , )| | | ) max = − × (1) Smax is the maximum size of annotation among all GO terms in a DAG structure. If two proteins x and y are annotated on a more specific GO term and more common GO terms than x and z, then x is semantically more similar to y than z. Considering the graph topology, we also involve the topology weight. For an input graph G = (V, E), we assign the topology weight of an edge [u, v] to be the number of neighbors shared by the vertices u and v. Then we assigned the sum of Ssem(u, v) and topology weight to the edge between u and v as a weight. Weighted vertex and selecting seed We define the weight of each vertex to be the sum of the weights of its incident edges. After all vertices are assigned weights, we also sort in non-increasing order the vertices by their weights and store them in a queue Sq (vertices of the same weight are ordered in terms of their degrees).

LATER¶62-66

Examples of predicted complexes From the results of the experiment above we know that most of our predicted clusters have highly biological meanings. We give some examples of predicted clusters with detailed information which are matched with the benchmark complexes in Table 3. We also list the best matched benchmark complexes, NA and the p-value of three protein annotation. Some of predicted clusters from our method are not matched with complex from criterion dataset. But we find that they have highly biologically significant and have high local density, so some of them may be real complexes which are still undiscovered. We also give some examples in Table 4 and Figure 4. Their p-value of biological annotation shows that some of them may be the candidate protein complexes. The results are useful for biologists to find the new protein complexes. Conclusions It is believed that identification of protein complexes is useful to explain certain biological progress and to predict functions of proteins. In this paper, we developed an algorithm OIIP to identify protein complexes based on the new large weighted protein interaction networks. Experimentally generated protein-protein interaction data includes an enormous amount of false positives. So we introduced a semantic similarity method to measure the reliability of interactions. For this measurement, we use the annotations in Gene Ontology (GO), which provides the comprehensive functional information. When we implemented the OIIP algorithm with weighted networks, the overall F-measure and accuracy of complexes is substantially improved. This result strongly appeals the necessity of integrating of functional information for the analysis of protein-protein interaction data. The fact that biological properties are poor at the identification reveals that the higher-level structures (e. g., secondary and tertiary structure) of proteins cannot be accurately represented by the primary structure under the current coding techniques. The experimentally determined protein interaction network has not been used in the research, and a possible future research could combine the experimentally determined protein interactions with the GO estimated interactions to further improve the identification.

LATER¶62-66

Examples of predicted complexes From the results of the experiment above we know that most of our predicted clusters have highly biological meanings. We give some examples of predicted clusters with detailed information which are matched with the benchmark complexes in Table 3. We also list the best matched benchmark complexes, NA and the p-value of three protein annotation. Some of predicted clusters from our method are not matched with complex from criterion dataset. But we find that they have highly biologically significant and have high local density, so some of them may be real complexes which are still undiscovered. We also give some examples in Table 4 and Figure 4. Their p-value of biological annotation shows that some of them may be the candidate protein complexes. The results are useful for biologists to find the new protein complexes. Conclusions It is believed that identification of protein complexes is useful to explain certain biological progress and to predict functions of proteins. In this paper, we developed an algorithm OIIP to identify protein complexes based on the new large weighted protein interaction networks. Experimentally generated protein-protein interaction data includes an enormous amount of false positives. So we introduced a semantic similarity method to measure the reliability of interactions. For this measurement, we use the annotations in Gene Ontology (GO), which provides the comprehensive functional information. When we implemented the OIIP algorithm with weighted networks, the overall F-measure and accuracy of complexes is substantially improved. This result strongly appeals the necessity of integrating of functional information for the analysis of protein-protein interaction data. The fact that biological properties are poor at the identification reveals that the higher-level structures (e. g., secondary and tertiary structure) of proteins cannot be accurately represented by the primary structure under the current coding techniques. The experimentally determined protein interaction network has not been used in the research, and a possible future research could combine the experimentally determined protein interactions with the GO estimated interactions to further improve the identification.

LATER¶62-66

Examples of predicted complexes From the results of the experiment above we know that most of our predicted clusters have highly biological meanings. We give some examples of predicted clusters with detailed information which are matched with the benchmark complexes in Table 3. We also list the best matched benchmark complexes, NA and the p-value of three protein annotation. Some of predicted clusters from our method are not matched with complex from criterion dataset. But we find that they have highly biologically significant and have high local density, so some of them may be real complexes which are still undiscovered. We also give some examples in Table 4 and Figure 4. Their p-value of biological annotation shows that some of them may be the candidate protein complexes. The results are useful for biologists to find the new protein complexes. Conclusions It is believed that identification of protein complexes is useful to explain certain biological progress and to predict functions of proteins. In this paper, we developed an algorithm OIIP to identify protein complexes based on the new large weighted protein interaction networks. Experimentally generated protein-protein interaction data includes an enormous amount of false positives. So we introduced a semantic similarity method to measure the reliability of interactions. For this measurement, we use the annotations in Gene Ontology (GO), which provides the comprehensive functional information. When we implemented the OIIP algorithm with weighted networks, the overall F-measure and accuracy of complexes is substantially improved. This result strongly appeals the necessity of integrating of functional information for the analysis of protein-protein interaction data. The fact that biological properties are poor at the identification reveals that the higher-level structures (e. g., secondary and tertiary structure) of proteins cannot be accurately represented by the primary structure under the current coding techniques. The experimentally determined protein interaction network has not been used in the research, and a possible future research could combine the experimentally determined protein interactions with the GO estimated interactions to further improve the identification.