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Open Access

Computational Approaches for Prioritizing Candidate Disease Genes Based on PPI Networks

Wei Lan, Jianxin Wang( ), Min Li( ), Wei Peng, Fangxiang Wu
School of Information Science and Engineering, Central South University, Changsha 410083, China.
Department of Mechanical Engineering and Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada.
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Abstract

With the continuing development and improvement of genome-wide techniques, a great number of candidate genes are discovered. How to identify the most likely disease genes among a large number of candidates becomes a fundamental challenge in human health. A common view is that genes related to a specific or similar disease tend to reside in the same neighbourhood of biomolecular networks. Recently, based on such observations, many methods have been developed to tackle this challenge. In this review, we firstly introduce the concept of disease genes, their properties, and available data for identifying them. Then we review the recent computational approaches for prioritizing candidate disease genes based on Protein-Protein Interaction (PPI) networks and investigate their advantages and disadvantages. Furthermore, some pieces of existing software and network resources are summarized. Finally, we discuss key issues in prioritizing candidate disease genes and point out some future research directions.

Keywords

candidate disease-gene prioritizationprotein-protein interaction networkhuman diseasecomputational tools

References

[1]
Tenesa A. and Haley C. S., The heritability of human disease: Estimation, uses and abuses, Nature Reviews Genetics, vol. 14, no. 2, pp. 139-149, 2013.
Crossref Google Scholar
[2]
Masoudi-Nejad A., Meshkin A., Haji-Eghrari B., and Bidkhori G., Candidate gene prioritization, Molecular Genetics and Genomics, vol. 287, no. 9, pp. 679-698, 2012.
Crossref Google Scholar
[3]
Walker F. O., Huntington’s disease, The Lancet, vol. 369, no. 9557, pp. 218-228, 2007.
Crossref Google Scholar
[4]
Altshuler D., Daly M. J., and Lander E. S., Genetic mapping in human disease, Science, vol. 322, no. 5903, pp. 881-888, 2008.
Crossref Google Scholar
[5]
Wang J., Chen G., Li M., and Pan Y., Integration of breast cancer gene signatures based on graph centrality, BMC Systems Biology, vol. 5, no. Suppl 3, p. S10, 2011.
Crossref Google Scholar
[6]
Jiang L., Edwards S. M., Thomsen B., Workman C. T., Guldbrandtsen B., and Sørensen P., A random set scoring model for prioritization of disease candidate genes using protein complexes and data-mining of generif, omim and pubmed records, BMC Bioinformatics, vol. 15, no. 1, p. 315, 2014.
Crossref Google Scholar
[7]
Valentini G., Paccanaro A., Caniza H., Romero A. E., and Re M., An extensive analysis of disease-gene associations using network integration and fast kernel-based gene prioritization methods, Artificial Intelligence in Medicine, vol. 61, no. 2, pp. 63-78, 2014.
Crossref Google Scholar
[8]
Wang J., Li M., Chen J., and Pan Y., A fast hierarchical clustering algorithm for functional modules discovery in protein interaction networks, Computational Biology and Bioinformatics, IEEE/ACM Transactions on, vol. 8, no. 3, pp. 607-620, 2011.
Crossref Google Scholar
[9]
Peng W., Wang J., Cai J., Chen L., Li M., and Wu F.-X., Improving protein function prediction using domain and protein complexes in ppi networks, BMC Systems Biology, vol. 8, no. 1, p. 35, 2014.
Crossref Google Scholar
[10]
Xiong W., Liu H., Guan J., and Zhou S., Protein function prediction by collective classification with explicit and implicit edges in protein-protein interaction networks, BMC Bioinformatics, vol. 14, no. Suppl 12, p. S4, 2013.
Crossref Google Scholar
[11]
Zhao B., Wang J., Li M., Wu F.-X., and Pan Y., Detecting protein complexes based on uncertain graph model, Computational Biology and Bioinformatics, IEEE/ACM Transactions on, vol. 11, no. 3, pp. 486-497, 2014.
Crossref Google Scholar
[12]
Yang Z. H., Feng Y. Y., Lin H. F., and Wang J., Integrating ppi datasets with the ppi data from biomedical literature for protein complex detection, BMC Medical Genomics, vol. 7, no. Suppl 2, p. S3, 2014.
Crossref Google Scholar
[13]
Li M., Zheng R., Zhang H., Wang J., and Pan Y., Effective identification of essential proteins based on priori knowledge, network topology and gene expressions, Methods, vol. 67, no. 3, pp. 325-333, 2014.
Crossref Google Scholar
[14]
Yang L., Wang J., Wang H., Lv Y., Zuo Y., Li X., and Jiang W., Analysis and identification of essential genes in humans using topological properties and biological information, Gene, vol. 551, no. 2, pp. 138-151, 2014.
Crossref Google Scholar
[15]
Wang J., Huang Y., Wu F.-X., and Pan Y., Symmetry compression method for discovering network motifs, Computational Biology and Bioinformatics, IEEE/ACM Transactions on, vol. 9, no. 6, pp. 1776-1789, 2012.
Crossref Google Scholar
[16]
Piro R. M. and Di Cunto F., Computational approaches to disease-gene prediction: Rationale, classification and successes, FEBS Journal, vol. 279, no. 5, pp. 678-696, 2012.
Crossref Google Scholar
[17]
Zhu C., Wu C., Aronow B. J., and Jegga A. G., Computational approaches for human disease gene prediction and ranking, in Systems Analysis of Human Multigene Disorders. Springer, 2014, pp. 69-84.
Crossref
[18]
Amberger J., Bocchini C., and Hamosh A., A new face and new challenges for online mendelian inheritance in man (omim®), Human Mutation, vol. 32, no. 5, pp. 564-567, 2011.
Crossref Google Scholar
[19]
Becker K. G., Barnes K. C., Bright T. J., and Wang S. A., The genetic association database, Nature Genetics, vol. 36, no. 5, pp. 431-432, 2004.
Crossref Google Scholar
[20]
Zhang Y., De S., Garner J. R., Smith K., Wang S. A., and Becker K. G., Systematic analysis, comparison, and integration of disease based human genetic association data and mouse genetic phenotypic information, BMC Medical Genomics, vol. 3, no. 1, p. 1, 2010.
Crossref Google Scholar
[21]
Bauer-Mehren A., Bundschus M., Rautschka M., Mayer M. A., Sanz F., and Furlong L. I., Gene-disease network analysis reveals functional modules in mendelian, complex and environmental diseases, PloS One, vol. 6, no. 6, p. e20284, 2011.
Crossref Google Scholar
[22]
Ewing R. M., Chu P., Elisma F., Li H., Taylor P., Climie S., McBroom-Cerajewski L., Robinson M. D., O’Connor L., Li M., et al., Large-scale mapping of human protein-protein interactions by mass spectrometry, Molecular Systems Biology, vol. 3, no. 1, p. 89, 2007.
Crossref Google Scholar
[23]
Dreze M., Monachello D., Lurin C., Cusick M. E., Hill D. E., Vidal M., and Braun P., High-quality binary interactome mapping, Methods in Enzymology, vol. 470, pp. 281-315, 2010.
Crossref Google Scholar
[24]
Ding X., Wang W., Peng X., and Wang J., Mining protein complexes from ppi networks using the minimum vertex cut, Tsinghua Science and Technology, vol. 17, no. 6, pp. 674-681, 2012.
Crossref Google Scholar
[25]
Cusick M. E., Yu H., Smolyar A., Venkatesan K., Carvunis A.-R., Simonis N., Rual J.-F., Borick H., Braun P., Dreze M., et al., Literature-curated protein interaction datasets, Nature Methods, vol. 6, no. 1, pp. 39-46, 2009.
Crossref Google Scholar
[26]
Bader G. D., Betel D., and Hogue C. W., Bind: The biomolecular interaction network database, Nucleic Acids Research, vol. 31, no. 1, pp. 248-250, 2003.
Crossref Google Scholar
[27]
Salwinski L., Miller C. S., Smith A. J., Pettit F. K., Bowie J. U., and Eisenberg D., The database of interacting proteins: 2004 update, Nucleic Acids Research, vol. 32, no. suppl 1, pp. D449-D451, 2004.
Crossref Google Scholar
[28]
Licata L., Briganti L., Peluso D., Perfetto L., Iannuccelli M., Galeota E., Sacco F., Palma A., Nardozza A. P., Santonico E., et al., Mint, the molecular interaction database: 2012 update, Nucleic Acids Research, vol. 40, no. D1, pp. D857-D861, 2012.
Crossref Google Scholar
[29]
Kerrien S., Aranda B., Breuza L., Bridge A., Broackes-Carter F., Chen C., Duesbury M., Dumousseau M., Feuermann M., Hinz U., et al., The intact molecular interaction database in 2012, Nucleic Acids Research, p. gkr1088, 2011.
Crossref Google Scholar
[30]
Stark C., Breitkreutz B.-J., Reguly T., Boucher L., Breitkreutz A., and Tyers M., Biogrid: A general repository for interaction datasets, Nucleic Acids Research, vol. 34, no. suppl 1, pp. D535-D539, 2006.
Crossref Google Scholar
[31]
Prasad T. K., Goel R., Kandasamy K., Keerthikumar S., Kumar S., Mathivanan S., Telikicherla D., Raju R., Shafreen B., Venugopal A., et al., Human protein reference database-2009 update, Nucleic Acids Research, vol. 37, no. suppl 1, pp. D767-D772, 2009.
Crossref Google Scholar
[32]
Franceschini A., Szklarczyk D., Frankild S., Kuhn M., Simonovic M., Roth A., Lin J., Minguez P., Bork P., von Mering C., et al., String v9. 1: Protein-protein interaction networks, with increased coverage and integration, Nucleic Acids Research, vol. 41, no. D1, pp. D808-D815, 2013.
Crossref Google Scholar
[33]
Schaefer M. H., Fontaine J.-F., Vinayagam A., Porras P., Wanker E. E., and Andrade-Navarro M. A., Hippie: Integrating protein interaction networks with experiment based quality scores, PloS One, vol. 7, no. 2, p. e31826, 2012.
Crossref Google Scholar
[34]
Barabási A.-L., Gulbahce N., and Loscalzo J., Network medicine: A network-based approach to human disease, Nature Reviews Genetics, vol. 12, no. 1, pp. 56-68, 2011.
Crossref Google Scholar
[35]
Wysocki K. and Ritter L., Diseasome an approach to understanding gene-disease interactions, Annual Review of Nursing Research, vol. 29, no. 1, pp. 55-72, 2011.
Crossref Google Scholar
[36]
Tang H., Zhong F., and Xie H., A quick guide to biomolecular network studies: Construction, analysis, applications, and resources, Biochemical and Biophysical Research Communications, vol. 424, no. 1, pp. 7-11, 2012.
Crossref Google Scholar
[37]
Oti M., Snel B., Huynen M. A., and Brunner H. G., Predicting disease genes using protein-protein interactions, Journal of Medical Genetics, vol. 43, no. 8, pp. 691-698, 2006.
Crossref Google Scholar
[38]
Hsu C.-L., Huang Y.-H., Hsu C.-T., and Yang U.-C., Prioritizing disease candidate genes by a gene interconnectedness-based approach, BMC Genomics, vol. 12, no. Suppl 3, p. S25, 2011.
Crossref Google Scholar
[39]
Zhu C., Kushwaha A., Berman K., and Jegga A. G., A vertex similarity-based framework to discover and rank orphan disease-related genes, BMC Systems Biology, vol. 6, no. Suppl 3, p. S8, 2012.
Crossref Google Scholar
[40]
Li M., Li Q., Ganegoda G. U., Wang J., Wu F., and Pan Y., Prioritization of orphan disease-causing genes using topological feature and go similarity between proteins in interaction networks, Science China Life Sciences, vol. 57, no. 11, pp. 1064-1071, 2014.
Crossref Google Scholar
[41]
Köhler S., Bauer S., Horn D., and Robinson P. N., Walking the interactome for prioritization of candidate disease genes, The American Journal of Human Genetics, vol. 82, no. 4, pp. 949-958, 2008.
Crossref Google Scholar
[42]
Erten S., Bebek G., and Koyutürk M., Vavien: An algorithm for prioritizing candidate disease genes based on topological similarity of proteins in interaction networks, Journal of Computational Biology, vol. 18, no. 11, pp. 1561-1574, 2011.
Crossref Google Scholar
[43]
Le D.-H. and Kwon Y.-K., Neighbor-favoring weight reinforcement to improve random walk-based disease gene prioritization, Computational Biology and Chemistry, vol. 44, pp. 1-8, 2013.
Crossref Google Scholar
[44]
Zhang S.-W., Shao D.-D., Zhang S.-Y., and Wang Y.-B., Prioritization of candidate disease genes by enlarging the seed set and fusing information of the network topology and gene expression, Molecular BioSystems, vol. 10, no. 6, pp. 1400-1408, 2014.
Crossref Google Scholar
[45]
Wang J., Peng X., Peng W., and Wu F.-X., Dynamic protein interaction network construction and applications, Proteomics, vol. 14, nos. 4&5, pp. 338-352, 2014.
Crossref Google Scholar
[46]
Tang X., Wang J., Liu B., Li M., Chen G., and Pan Y., A comparison of the functional modules identified from time course and static ppi network data, BMC Bioinformatics, vol. 12, no. 1, p. 339, 2011.
Crossref Google Scholar
[47]
Wodak S. J., Vlasblom J., Turinsky A. L., and Pu S., Protein-protein interaction networks: The puzzling riches, Current Opinion in Structural Biology, vol. 23, no. 6, pp. 941-953, 2013.
Crossref Google Scholar
[48]
Li M., Wu X., Wang J., and Pan Y., Towards the identification of protein complexes and functional modules by integrating ppi network and gene expression data, BMC Bioinformatics, vol. 13, no. 1, p. 109, 2012.
Crossref Google Scholar
[49]
Wang J., Zhang S., Wang Y., Chen L., and Zhang X.-S., Disease-aging network reveals significant roles of aging genes in connecting genetic diseases, PLoS Comput. Biol., vol. 5, no. 9, p. e1000521, 2009.
Crossref Google Scholar
[50]
Lee S.-A., Tsao T. T., Yang K.-C., Lin H., Kuo Y.-L., Hsu C.-H., Lee W.-K., Huang K.-C., and Kao C.-Y., Construction and analysis of the protein-protein interaction networks for schizophrenia, bipolar disorder, and major depression, BMC Bioinformatics, vol. 12, no. Suppl 13, p. S20, 2011.
Crossref Google Scholar
[51]
Zhao J., Chen J., Yang T.-H., and Holme P., Insights into the pathogenesis of axial spondyloarthropathy from network and pathway analysis, BMC Systems Biology, vol. 6, no. Suppl 1, p. S4, 2012.
Crossref Google Scholar
[52]
He D., Liu Z.-P., and Chen L., Identification of dysfunctional modules and disease genes in congenital heart disease by a network-based approach, BMC Genomics, vol. 12, no. 1, p. 592, 2011.
Crossref Google Scholar
[53]
Lee E., Jung H., Radivojac P., Kim J.-W., and Lee D., Analysis of aml genes in dysregulated molecular networks, BMC Bioinformatics, vol. 10, no. Suppl 9, p. S2, 2009.
Crossref Google Scholar
[54]
Zhang X., Zhang R., Jiang Y., Sun P., Tang G., Wang X., Lv H., and Li X., The expanded human disease network combining protein-protein interaction information, European Journal of Human Genetics, vol. 19, no. 7, pp. 783-788, 2011.
Crossref Google Scholar
[55]
Hwang S., Son S.-W., Kim S. C., Kim Y. J., Jeong H., and Lee D., A protein interaction network associated with asthma, Journal of Theoretical Biology, vol. 252, no. 4, pp. 722-731, 2008.
Crossref Google Scholar
[56]
Magger O., Waldman Y. Y., Ruppin E., and Sharan R., Enhancing the prioritization of disease-causing genes through tissue specific protein interaction networks, PLoS Comput. Biol., vol. 8, no. 9, p. e1002690, 2012.
Crossref Google Scholar
[57]
Li M., Zhang J., Liu Q., Wang J., and Wu F.-X., Prediction of disease-related genes based on weighted tissue-specific networks by using dna methylation, BMC Medical Genomics, vol. 7, no. Suppl 2, p. S4, 2014.
Crossref Google Scholar
[58]
Wang X., Gulbahce N., and Yu H., Network-based methods for human disease gene prediction, Briefings in Functional Genomics, vol. 10, no. 5, pp. 280-293, 2011.
Crossref Google Scholar
[59]
Wang J., Peng X., Li M., and Pan Y., Construction and application of dynamic protein interaction network based on time course gene expression data, Proteomics, vol. 13, no. 2, pp. 301-312, 2013.
Crossref Google Scholar
[60]
Wu X., Jiang R., Zhang M. Q., and Li S., Network-based global inference of human disease genes, Molecular Systems Biology, vol. 4, no. 1, p. 189, 2008.
Crossref Google Scholar
[61]
Zhang W., Sun F., and Jiang R., Integrating multiple protein-protein interaction networks to prioritize disease genes: A bayesian regression approach, BMC Bioinformatics, vol. 12, no. Suppl 1, p. S11, 2011.
Crossref Google Scholar
[62]
Yao X., Hao H., Li Y., and Li S., Modularity-based credible prediction of disease genes and detection of disease subtypes on the phenotype-gene heterogeneous network, BMC Systems Biology, vol. 5, no. 1, p. 79, 2011.
Crossref Google Scholar
[63]
Yang P., Li X., Wu M., Kwoh C.-K., and Ng S.-K., Inferring gene-phenotype associations via global protein complex network propagation, PloS One, vol. 6, no. 7, p. e21502, 2011.
Crossref Google Scholar
[64]
Li M., Chen J., Wang J., Hu B., and Chen G., Modifying the dpclus algorithm for identifying protein complexes based on new topological structures, BMC Bioinformatics, vol. 9, no. 1, p. 398, 2008.
Crossref Google Scholar
[65]
Lu C., Xiao C., Chen G., Jiang M., Zha Q., Yan X., Kong W., and Lu A., Cold and heat pattern of rheumatoid arthritis in traditional chinese medicine: Distinct molecular signatures indentified by microarray expression profiles in cd4-positive t cell, Rheumatology International, vol. 32, no. 1, pp. 61-68, 2012.
Crossref Google Scholar
[66]
Li Y. and Li J., Disease gene identification by random walk on multigraphs merging heterogeneous genomic and phenotype data, BMC Genomics, vol. 13, no. Suppl 7, p. S27, 2012.
Crossref Google Scholar
[67]
Xie M., Hwang T., and Kuang R., Prioritizing disease genes by bi-random walk, in Advances in Knowledge Discovery and Data Mining. Springer, 2012, pp. 292-303.
Crossref
[68]
Vanunu O., Magger O., Ruppin E., Shlomi T., and Sharan R., Associating genes and protein complexes with disease via network propagation, PhD dissertation, Tel Aviv University, Israel, 2009.
Crossref
[69]
Guo X., Gao L., Wei C., Yang X., Zhao Y., and Dong A., A computational method based on the integration of heterogeneous networks for predicting disease-gene associations, PloS One, vol. 6, no. 9, p. e34171, 2011.
Crossref Google Scholar
[70]
Ganegoda G. U., Wang J., Wu F.-X., and Li M., Prediction of disease genes using tissue-specified gene-gene network, BMC Systems Biology, vol. 8, no. Suppl 3, p. S3, 2014.
Crossref Google Scholar
[71]
Chen Y., Jiang T., and Jiang R., Uncover disease genes by maximizing information flow in the phenome-interactome network, Bioinformatics, vol. 27, no. 13, pp. i167-i176, 2011.
Crossref Google Scholar
[72]
Wu X., Liu Q., and Jiang R., Align human interactome with phenome to identify causative genes and networks underlying disease families, Bioinformatics, vol. 25, no. 1, pp. 98-104, 2009.
Crossref Google Scholar
[73]
Huang D. W., Sherman B. T., and Lempicki R. A., Systematic and integrative analysis of large gene lists using david bioinformatics resources, Nature Protocols, vol. 4, no. 1, pp. 44-57, 2008.
Crossref Google Scholar
[74]
Li Y. and Patra J. C., Integration of multiple data sources to prioritize candidate genes using discounted rating system, BMC Bioinformatics, vol. 11, no. Suppl 1, p. S20, 2010.
Crossref Google Scholar
[75]
Chen Y., Wang W., Zhou Y., Shields R., Chanda S. K., Elston R. C., and Li J., In silico gene prioritization by integrating multiple data sources, PLoS One, vol. 6, no. 6, p. e21137, 2011.
Crossref Google Scholar
[76]
Hindumathi V., Kranthi T., Rao S., and Manimaran P., The prediction of candidate genes for cervix related cancer through gene ontology and graph theoretical approach, Molecular BioSystems, vol. 10, no. 6, pp. 1450-1460, 2014.
Crossref Google Scholar
[77]
Jia P., Kao C.-F., Kuo P.-H., and Zhao Z., A comprehensive network and pathway analysis of candidate genes in major depressive disorder, BMC Systems Biology, vol. 5, no. Suppl 3, p. S12, 2011.
Crossref Google Scholar
[78]
Nitsch D., Gonçalves J. P., Ojeda F., De Moor B., and Moreau Y., Candidate gene prioritization by network analysis of differential expression using machine learning approaches, BMC Bioinformatics, vol. 11, no. 1, p. 460, 2010.
Crossref Google Scholar
[79]
Chen B., Wang J., Li M., and Wu F.-X., Identifying disease genes by integrating multiple data sources, BMC Medical Genomics, vol. 7, no. Suppl 2, p. S2, 2014.
Crossref Google Scholar
[80]
Chen B., Li M., Wang J., and Wu F.-X., Disease gene identification by using graph kernels and markov random fields, Science China Life Sciences, vol. 57, no. 11, pp. 1054-1063, 2014.
Crossref Google Scholar
[81]
Chen Y., Wu X., and Jiang R., Integrating human omics data to prioritize candidate genes, BMC Medical Genomics, vol. 6, no. 1, p. 57, 2013.
Crossref Google Scholar
[82]
Nguyen T.-P. and Ho T.-B., Detecting disease genes based on semi-supervised learning and protein-protein interaction networks, Artificial Intelligence in Medicine, vol. 54, no. 1, pp. 63-71, 2012.
Crossref Google Scholar
[83]
Mordelet F. and Vert J.-P., Prodige: Prioritization of disease genes with multitask machine learning from positive and unlabeled examples, BMC Bioinformatics, vol. 12, no. 1, p. 389, 2011.
Crossref Google Scholar
[84]
Wang J., Peng W., and Wu F.-X., Computational approaches to predicting essential proteins: A survey, PROTEOMICS-Clinical Applications, vol. 7, nos. 1&2, pp. 181-192, 2013.
Crossref Google Scholar
[85]
Wang J., Li M., Wang H., and Pan Y., Identification of essential proteins based on edge clustering coefficient, Computational Biology and Bioinformatics, IEEE/ACM Transactions on, vol. 9, no. 4, pp. 1070-1080, 2012.
Crossref Google Scholar
[86]
Tang X., Wang J., Zhong J., and Pan Y., Predicting essential proteins based on weighted degree centrality, Computational Biology and Bioinformatics, IEEE/ACM Transactions on, vol. 11, no. 2, pp. 407-418, 2014.
Crossref Google Scholar
[87]
Li M., Wang J., Chen X., Wang H., and Pan Y., A local average connectivity-based method for identifying essential proteins from the network level, Computational Biology and Chemistry, vol. 35, no. 3, pp. 143-150, 2011.
Crossref Google Scholar
[88]
Börnigen D., Tranchevent L.-C., Bonachela-Capdevila F., Devriendt K., De Moor B., De Causmaecker P., and Moreau Y., An unbiased evaluation of gene prioritization tools, Bioinformatics, vol. 28, no. 23, pp. 3081-3088, 2012.
Crossref Google Scholar
[89]
Tranchevent L.-C., Capdevila F. B., Nitsch D., De Moor B., De Causmaecker P., and Moreau Y., A guide to web tools to prioritize candidate genes, Briefings in Bioinformatics, vol. 12, no. 1, pp. 22-32, 2011.
Crossref Google Scholar
[90]
Tang Y., Li M., Wang J., Pan Y., and Wu F.-X., Cytonca: A cytoscape plugin for centrality analysis and evaluation of protein interaction networks, BioSystems, vol. 127, pp. 67-72, 2015.
Crossref Google Scholar
[91]
Chen J., Bardes E. E., Aronow B. J., and Jegga A. G., Toppgene suite for gene list enrichment analysis and candidate gene prioritization, Nucleic Acids Research, vol. 37, no. suppl 2, pp. W305-W311, 2009.
Crossref Google Scholar
[92]
Nitsch D., Tranchevent L.-C., Goncalves J. P., Vogt J. K., Madeira S. C., and Moreau Y., Pinta: A web server for network-based gene prioritization from expression data, Nucleic Acids Research, vol. 39, no. suppl 2, pp. W334-W338, 2011.
Crossref Google Scholar
[93]
George R. A., Liu J. Y., Feng L. L., Bryson-Richardson R. J., Fatkin D., and Wouters M. A., Analysis of protein sequence and interaction data for candidate disease gene prediction, Nucleic Acids Research, vol. 34, no. 19, pp. e130-e130, 2006.
Crossref Google Scholar
[94]
Pers T. H., Hansen N. T., Lage K., Koefoed P., Dworzynski P., Miller M. L., Flint T. J., Mellerup E., Dam H., Andreassen O. A., et al., Meta-analysis of heterogeneous data sources for genome-scale identification of risk genes in complex phenotypes, Genetic Epidemiology, vol. 35, no. 5, pp. 318-332, 2011.
Crossref Google Scholar
[95]
Aerts S., Lambrechts D., Maity S., Van Loo P., Coessens B., De Smet F., Tranchevent L.-C., De Moor B., Marynen P., Hassan B., et al., Gene prioritization through genomic data fusion, Nature Biotechnology, vol. 24, no. 5, pp. 537-544, 2006.
Crossref Google Scholar
[96]
Seelow D., Schwarz J. M., and Schuelke M., Genedistiller-distilling candidate genes from linkage intervals, PLoS One, vol. 3, no. 12, p. e3874, 2008.
Crossref Google Scholar
[97]
Guney E., Garcia-Garcia J., and Oliva B., Guildify: A web server for phenotypic characterization of genes through biological data integration and network-based prioritization algorithms, Bioinformatics, vol. 30, no. 12, pp. 1789-1790, 2014.
Crossref Google Scholar
[98]
Martínez V., Cano C., and Blanco A., Prophnet: A generic prioritization method through propagation of information, BMC Bioinformatics, vol. 15, no. Suppl 1, p. S5, 2014.
Crossref Google Scholar
[99]
Bamshad M. J., Ng S. B., Bigham A. W., Tabor H. K., Emond M. J., Nickerson D. A., and Shendure J., Exome sequencing as a tool for mendelian disease gene discovery, Nature Reviews Genetics, vol. 12, no. 11, pp. 745-755, 2011.
Crossref Google Scholar
[100]
O’Connor T. P. and Crystal R. G., Genetic medicines: Treatment strategies for hereditary disorders, Nature Reviews Genetics, vol. 7, no. 4, pp. 261-276, 2006.
Crossref Google Scholar
[101]
Li M., Wang J., Chen J., Cai Z., and Chen G., Identifying the overlapping complexes in protein interaction networks, International Journal of Data Mining and Bioinformatics, vol. 4, no. 1, pp. 91-108, 2010.
Crossref Google Scholar
[102]
Zaki N., Efimov D., and Berengueres J., Protein complex detection using interaction reliability assessment and weighted clustering coefficient, BMC Bioinformatics, vol. 14, no. 1, p. 163, 2013.
Crossref Google Scholar
[103]
Peng W., Wang J., Wang W., Liu Q., Wu F.-X., and Pan Y., Iteration method for predicting essential proteins based on orthology and protein-protein interaction networks, BMC Systems Biology, vol. 6, no. 1, p. 87, 2012.
Crossref Google Scholar
[104]
Li M., Lu Y., Wang J., Wu F.-X., and Pan Y., A topology potential-based method for identifying essential proteins from ppi networks, Computational Biology and Bioinformatics, IEEE/ACM Transactions on, vol. 12, no. 2, pp. 372-383, 2015.
Crossref Google Scholar
[105]
Moreau Y. and Tranchevent L.-C., Computational tools for prioritizing candidate genes: Boosting disease gene discovery, Nature Reviews Genetics, vol. 13, no. 8, pp. 523-536, 2012.
Crossref Google Scholar
[106]
Wang J., Li M., Deng Y., and Pan Y., Recent advances in clustering methods for protein interaction networks, BMC Genomics, vol. 11, no. Suppl 3, p. S10, 2010.
Crossref Google Scholar
[107]
Farh K. K.-H., Marson A., Zhu J., Kleinewietfeld M., Housley W. J., Beik S., Shoresh N., Whitton H., Ryan R. J., Shishkin A. A., et al., Genetic and epigenetic fine mapping of causal autoimmune disease variants, Nature, vol. 518, no. 7539, pp. 337-343, 2015.
Crossref Google Scholar
[108]
Chen Q., Lan W., and Wang J., Mining featured patterns of mirna interaction based on sequence and structure similarity, Computational Biology and Bioinformatics, IEEE/ACM Transactions on, vol. 10, no. 2, pp. 415-422, 2013.
Crossref Google Scholar
[109]
Wang J., Peng X., Xiao Q., Li M., and Pan Y., An effective method for refining predicted protein complexes based on protein activity and the mechanism of protein complex formation, BMC Systems Biology, vol. 7, no. 1, p. 28, 2013.
Crossref Google Scholar
[110]
Li M., Chen W., Wang J., Wu F.-X., and Pan Y., Identifying dynamic protein complexes based on gene expression profiles and ppi networks, BioMed Research International, vol. 2014, p. 375262, 2014.
Crossref Google Scholar
Tsinghua Science and Technology
Volume 20 Issue 5,
October 2015
Pages 500-512
DOI: 10.1109/TST.2015.7297749
Cite this article:
Lan W, Wang J, Li M, et al. Computational Approaches for Prioritizing Candidate Disease Genes Based on PPI Networks. Tsinghua Science and Technology, 2015, 20(5): 500-512. https://doi.org/10.1109/TST.2015.7297749

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Received: 06 July 2015
Accepted: 06 August 2015
Published: 13 October 2015
The author(s) 2015
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