Computational Approaches for Human Disease Gene Prediction and Ranking

  • Cheng Zhu
  • Chao Wu
  • Bruce J. Aronow
  • Anil G. Jegga
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 799)


While candidate gene association studies continue to be the most practical and frequently employed approach in disease gene investigation for complex disorders, selecting suitable genes to test is a challenge. There are several computational approaches available for selecting and prioritizing disease candidate genes. A majority of these tools are based on guilt-by-association principle where novel disease candidate genes are identified and prioritized based on either functional or topological similarity to known disease genes. In this chapter we review the prioritization criteria and the algorithms along with some use cases that demonstrate how these tools can be used for identifying and ranking human disease candidate genes.


Congenital Diaphragmatic Hernia Exome Sequencing Protein Interaction Network Congenital Diaphragmatic Hernia Neuronal Ceroid Lipofuscinosis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Supplementary material

216120_1_En_4_MOESM1_ESM.xlsx (497 kb)
20-Fine needle aspiration of solid component of complex nodu (XLSX 497 KB)


  1. 1.
    Adie EA, Adams RR, Evans KL, Porteous DJ, Pickard BS (2005) Speeding disease gene discovery by sequence based candidate prioritization. BMC Bioinformatics 6:55PubMedCrossRefGoogle Scholar
  2. 2.
    Adie EA, Adams RR, Evans KL, Porteous DJ, Pickard BS (2006) SUSPECTS: enabling fast and effective prioritization of positional candidates. Bioinformatics 22(6):773–774PubMedCrossRefGoogle Scholar
  3. 3.
    Aerts S, Lambrechts D, Maity S, Van Loo P, Coessens B, De Smet F, Tranchevent LC, De Moor B, Marynen P, Hassan B, Carmeliet P, Moreau Y (2006) Gene prioritization through genomic data fusion. Nat Biotechnol 24(5):537–544PubMedCrossRefGoogle Scholar
  4. 4.
    Becker KG, Barnes KC, Bright TJ, Wang SA (2004) The genetic association database. Nat Genet 36(5):431–432. doi: 10.1038/ng0504-431, ng0504-431 [pii]PubMedCrossRefGoogle Scholar
  5. 5.
    Benitez BA, Alvarado D, Cai Y, Mayo K, Chakraverty S, Norton J, Morris JC, Sands MS, Goate A, Cruchaga C (2011) Exome-sequencing confirms DNAJC5 mutations as cause of adult neuronal ceroid-lipofuscinosis. PLoS One 6(11):e26741. doi: 10.1371/journal.pone.0026741, PONE-D-11-16499 [pii]PubMedCrossRefGoogle Scholar
  6. 6.
    Beurskens LW, Tibboel D, Lindemans J, Duvekot JJ, Cohen-Overbeek TE, Veenma DC, de Klein A, Greer JJ, Steegers-Theunissen RP (2010) Retinol status of newborn infants is associated with congenital diaphragmatic hernia. Pediatrics 126(4):712–720. doi: 10.1542/peds.2010-0521, peds.2010-0521 [pii]PubMedCrossRefGoogle Scholar
  7. 7.
    Bornigen D, Tranchevent LC, Bonachela-Capdevila F, Devriendt K, De Moor B, De Causmaecker P, Moreau Y (2012) An unbiased evaluation of gene prioritization tools. Bioinformatics 28(23):3081–3088. doi: 10.1093/bioinformatics/bts581, bts581 [pii]PubMedCrossRefGoogle Scholar
  8. 8.
    Chen J, Aronow BJ, Jegga AG (2009) Disease candidate gene identification and prioritization using protein interaction networks. BMC Bioinformatics 10:73. doi: 1471-2105-10-73, [pii]  10.1186/1471-2105-10-73 PubMedCrossRefGoogle Scholar
  9. 9.
    Chen J, Bardes EE, Aronow BJ, Jegga AG (2009) ToppGene Suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Res 37(Web Server issue):W305–W311. doi: gkp427, [pii]  10.1093/nar/gkp427 PubMedCrossRefGoogle Scholar
  10. 10.
    Chen J, Xu H, Aronow BJ, Jegga AG (2007) Improved human disease candidate gene prioritization using mouse phenotype. BMC Bioinformatics 8(1):392PubMedCrossRefGoogle Scholar
  11. 11.
    Chen JY, Shen C, Sivachenko AY (2006) Mining Alzheimer disease relevant proteins from integrated protein interactome data. Pac Symp Biocomput 367–378Google Scholar
  12. 12.
    Chen X, Yan GY, Liao XP (2010) A novel candidate disease genes prioritization method based on module partition and rank fusion. OMICS 14(4):337–356. doi: 10.1089/omi.2009.0143 PubMedCrossRefGoogle Scholar
  13. 13.
    Davis AP, Murphy CG, Saraceni-Richards CA, Rosenstein MC, Wiegers TC, Mattingly CJ (2009) Comparative Toxicogenomics Database: a knowledgebase and discovery tool for chemical-gene-disease networks. Nucleic Acids Res 37(Database issue):D786–D792. doi: gkn580, [pii]  10.1093/nar/gkn580 PubMedCrossRefGoogle Scholar
  14. 14.
    Erlich Y, Edvardson S, Hodges E, Zenvirt S, Thekkat P, Shaag A, Dor T, Hannon GJ, Elpeleg O (2011) Exome sequencing and disease-network analysis of a single family implicate a mutation in KIF1A in hereditary spastic paraparesis. Genome Res 21(5):658–664. doi: gr.117143.110, [pii]  10.1101/gr.117143.110 PubMedCrossRefGoogle Scholar
  15. 15.
    Franke L, Bakel H, Fokkens L, de Jong ED, Egmont-Petersen M, Wijmenga C (2006) Reconstruction of a functional human gene network, with an application for prioritizing positional candidate genes. Am J Hum Genet 78(6):1011–1025PubMedCrossRefGoogle Scholar
  16. 16.
    Freudenberg J, Propping P (2002) A similarity-based method for genome-wide prediction of disease-relevant human genes. Bioinformatics 18(Suppl 2):S110–S115PubMedCrossRefGoogle Scholar
  17. 17.
    George RA, Liu JY, Feng LL, Bryson-Richardson RJ, Fatkin D, Wouters MA (2006) Analysis of protein sequence and interaction data for candidate disease gene prediction. Nucleic Acids Res 34(19):e130PubMedCrossRefGoogle Scholar
  18. 18.
    Giot L, Bader JS, Brouwer C, Chaudhuri A, Kuang B, Li Y, Hao YL, Ooi CE, Godwin B, Vitols E, Vijayadamodar G, Pochart P, Machineni H, Welsh M, Kong Y, Zerhusen B, Malcolm R, Varrone Z, Collis A, Minto M, Burgess S, McDaniel L, Stimpson E, Spriggs F, Williams J, Neurath K, Ioime N, Agee M, Voss E, Furtak K, Renzulli R, Aanensen N, Carrolla S, Bickelhaupt E, Lazovatsky Y, DaSilva A, Zhong J, Stanyon CA, Finley RL Jr, White KP, Braverman M, Jarvie T, Gold S, Leach M, Knight J, Shimkets RA, McKenna MP, Chant J, Rothberg JM (2003) A protein interaction map of Drosophila melanogaster. Science (New York, NY) 302(5651):1727–1736. doi: 10.1126/science.1090289, 1090289 [pii]CrossRefGoogle Scholar
  19. 19.
    Goehler H, Lalowski M, Stelzl U, Waelter S, Stroedicke M, Worm U, Droege A, Lindenberg KS, Knoblich M, Haenig C, Herbst M, Suopanki J, Scherzinger E, Abraham C, Bauer B, Hasenbank R, Fritzsche A, Ludewig AH, Bussow K, Coleman SH, Gutekunst CA, Landwehrmeyer BG, Lehrach H, Wanker EE (2004) A protein interaction network links GIT1, an enhancer of huntingtin aggregation, to Huntington's disease. Mol Cell 15(6):853–865. doi: 10.1016/j.molcel.2004.09.016, S1097276504005453 [pii]PubMedCrossRefGoogle Scholar
  20. 20.
    Goh KI, Cusick ME, Valle D, Childs B, Vidal M, Barabasi AL (2007) The human disease network. Proc Natl Acad Sci U S A 104(21):8685–8690. doi: 0701361104, [pii]  10.1073/pnas.0701361104 PubMedCrossRefGoogle Scholar
  21. 21.
    Hamosh A, Scott A, Amberger J, Bocchini C, McKusick V (2005) Online Mendelian inheritance in man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res 33:D514–D517PubMedCrossRefGoogle Scholar
  22. 22.
    Hindorff LA, Sethupathy P, Junkins HA, Ramos EM, Mehta JP, Collins FS, Manolio TA (2009) Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc Natl Acad Sci U S A 106(23):9362–9367. doi: 0903103106, [pii] 10.1073/pnas.0903103106 PubMedCrossRefGoogle Scholar
  23. 23.
    Hristovski D, Peterlin B, Mitchell JA, Humphrey SM (2005) Using literature-based discovery to identify disease candidate genes. Int J Med Inform 74(2–4):289–298PubMedCrossRefGoogle Scholar
  24. 24.
    Hsu C, Huang Y, Hsu C, Yang U (2011) Prioritizing disease candidate genes by a gene interconnectedness-based approach. BMC Genomics 12(3):S25PubMedCrossRefGoogle Scholar
  25. 25.
    Huynen MA, Snel B, van Noort V (2004) Comparative genomics for reliable protein-function prediction from genomic data. Trends Genet 20(8):340–344PubMedCrossRefGoogle Scholar
  26. 26.
    Ito T, Chiba T, Ozawa R, Yoshida M, Hattori M, Sakaki Y (2001) A comprehensive two-hybrid analysis to explore the yeast protein interactome. Proc Natl Acad Sci U S A 98(8):4569–4574. doi: 10.1073/pnas.061034498, 061034498 [pii]PubMedCrossRefGoogle Scholar
  27. 27.
    Jimenez-Sanchez G, Childs B, Valle D (2001) Human disease genes. Nature 409(6822): 853–855PubMedCrossRefGoogle Scholar
  28. 28.
    Junker BH, Koschutzki D, Schreiber F (2006) Exploration of biological network centralities with CentiBiN. BMC Bioinformatics 7:219PubMedCrossRefGoogle Scholar
  29. 29.
    Kaimal V, Sardana D, Bardes EE, Gudivada RC, Chen J, Jegga AG (2011) Integrative systems biology approaches to identify and prioritize disease and drug candidate genes. Methods Mol Biol 700:241–259. doi: 10.1007/978-1-61737-954-3_16 PubMedGoogle Scholar
  30. 30.
    Kann MG (2007) Protein interactions and disease: computational approaches to uncover the etiology of diseases. Brief Bioinform 8(5):333–346PubMedCrossRefGoogle Scholar
  31. 31.
    Kim YK, Wassef L, Hamberger L, Piantedosi R, Palczewski K, Blaner WS, Quadro L (2008) Retinyl ester formation by lecithin: retinol acyltransferase is a key regulator of retinoid homeostasis in mouse embryogenesis. J Biol Chem 283(9):5611–5621. doi: M708885200, [pii]  10.1074/jbc.M708885200 PubMedCrossRefGoogle Scholar
  32. 32.
    King MC, Wilson AC (1975) Evolution at two levels in humans and chimpanzees. Science (New York, NY) 188(4184):107–116CrossRefGoogle Scholar
  33. 33.
    Kleinberg J (1999) Authoritative sources in a hyperlinked environment. J ACM 46(5):604–632CrossRefGoogle Scholar
  34. 34.
    Kohler S, Bauer S, Horn D, Robinson PN (2008) Walking the interactome for prioritization of candidate disease genes. Am J Hum Genet 82(4):949–958. doi: S0002-9297(08)00172-9, [pii]  10.1016/j.ajhg.2008.02.013 PubMedCrossRefGoogle Scholar
  35. 35.
    Korstanje R, Paigen B (2002) From QTL to gene: the harvest begins. Nat Genet 31(3):235–236PubMedCrossRefGoogle Scholar
  36. 36.
    Lage K, Karlberg EO, Storling ZM, Olason PI, Pedersen AG, Rigina O, Hinsby AM, Tumer Z, Pociot F, Tommerup N et al (2007) A human phenome-interactome network of protein complexes implicated in genetic disorders. Nat Biotechnol 25(3):309–316PubMedCrossRefGoogle Scholar
  37. 37.
    Li S, Armstrong CM, Bertin N, Ge H, Milstein S, Boxem M, Vidalain PO, Han JD, Chesneau A, Hao T, Goldberg DS, Li N, Martinez M, Rual JF, Lamesch P, Xu L, Tewari M, Wong SL, Zhang LV, Berriz GF, Jacotot L, Vaglio P, Reboul J, Hirozane-Kishikawa T, Li Q, Gabel HW, Elewa A, Baumgartner B, Rose DJ, Yu H, Bosak S, Sequerra R, Fraser A, Mango SE, Saxton WM, Strome S, Van Den Heuvel S, Piano F, Vandenhaute J, Sardet C, Gerstein M, Doucette-Stamm L, Gunsalus KC, Harper JW, Cusick ME, Roth FP, Hill DE, Vidal M (2004) A map of the interactome network of the metazoan C. elegans. Science (New York, NY) 303(5657):540–543. doi: 10.1126/science.1091403, 1091403 [pii]CrossRefGoogle Scholar
  38. 38.
    Linghu B, Snitkin ES, Hu Z, Xia Y, Delisi C (2009) Genome-wide prioritization of disease genes and identification of disease-disease associations from an integrated human functional linkage network. Genome Biol 10(9):R91. doi: 10.1186/gb-2009-10-9-r91, gb-2009-10-9-r91 [pii]PubMedCrossRefGoogle Scholar
  39. 39.
    Lopez-Bigas N, Ouzounis CA (2004) Genome-wide identification of genes likely to be involved in human genetic disease. Nucleic Acids Res 32(10):3108–3114PubMedCrossRefGoogle Scholar
  40. 40.
    Mackay TF (2001) Quantitative trait loci in Drosophila. Nat Rev 2(1):11–20Google Scholar
  41. 41.
    Masseroli M, Galati O, Pinciroli F (2005) GFINDer: genetic disease and phenotype location statistical analysis and mining of dynamically annotated gene lists. Nucleic Acids Res 33(Web Server issue):W717–W723PubMedCrossRefGoogle Scholar
  42. 42.
    Masseroli M, Martucci D, Pinciroli F (2004) GFINDer: Genome Function INtegrated Discoverer through dynamic annotation, statistical analysis, and mining. Nucleic Acids Res 32(Web Server issue):W293–W300PubMedCrossRefGoogle Scholar
  43. 43.
    Moreau Y, Tranchevent LC (2012) Computational tools for prioritizing candidate genes: boosting disease gene discovery. Nat Rev 13(8):523–536. doi: 10.1038/nrg3253, nrg3253 [pii]CrossRefGoogle Scholar
  44. 44.
    Navlakha S, Kingsford C (2010) The power of protein interaction networks for associating genes with diseases. Bioinformatics 26(8):1057–1063. doi: 10.1093/bioinformatics/btq076, btq076 [pii]PubMedCrossRefGoogle Scholar
  45. 45.
    Ortutay C, Vihinen M (2009) Identification of candidate disease genes by integrating gene ontologies and protein-interaction networks: case study of primary immunodeficiencies. Nucleic Acids Res 37(2):622–628. doi: gkn982, [pii] 10.1093/nar/gkn982 PubMedCrossRefGoogle Scholar
  46. 46.
    Oti M, Ballouz S, Wouters MA (2011) Web tools for the prioritization of candidate disease genes. Methods Mol Biol 760:189–206. doi: 10.1007/978-1-61779-176-5_12 PubMedGoogle Scholar
  47. 47.
    Oti M, Snel B, Huynen MA, Brunner HG (2006) Predicting disease genes using protein-protein interactions. J Med Genet 43(8):691–698PubMedCrossRefGoogle Scholar
  48. 48.
    Perez-Iratxeta C, Bork P, Andrade MA (2002) Association of genes to genetically inherited diseases using data mining. Nat Genet 31(3):316–319PubMedGoogle Scholar
  49. 49.
    Perez-Iratxeta C, Wjst M, Bork P, Andrade MA (2005) G2D: a tool for mining genes associated with disease. BMC Genet 6:45PubMedCrossRefGoogle Scholar
  50. 50.
    Piro RM, Di Cunto F (2012) Computational approaches to disease-gene prediction: rationale, classification and successes. FEBS J 279(5):678–696. doi: 10.1111/j.1742-4658.2012.08471.x PubMedCrossRefGoogle Scholar
  51. 51.
    Popescu M, Keller JM, Mitchell JA (2006) Fuzzy measures on the gene ontology for gene product similarity. IEEE/ACM Trans Comput Biol Bioinform 3(3):263–274PubMedCrossRefGoogle Scholar
  52. 52.
    Rossi S, Masotti D, Nardini C, Bonora E, Romeo G, Macii E, Benini L, Volinia S (2006) TOM: a web-based integrated approach for identification of candidate disease genes. Nucleic Acids Res 34(Web Server issue):W285–W292PubMedCrossRefGoogle Scholar
  53. 53.
    Rual JF, Venkatesan K, Hao T, Hirozane-Kishikawa T, Dricot A, Li N, Berriz GF, Gibbons FD, Dreze M, Ayivi-Guedehoussou N, Klitgord N, Simon C, Boxem M, Milstein S, Rosenberg J, Goldberg DS, Zhang LV, Wong SL, Franklin G, Li S, Albala JS, Lim J, Fraughton C, Llamosas E, Cevik S, Bex C, Lamesch P, Sikorski RS, Vandenhaute J, Zoghbi HY, Smolyar A, Bosak S, Sequerra R, Doucette-Stamm L, Cusick ME, Hill DE, Roth FP, Vidal M (2005) Towards a proteome-scale map of the human protein-protein interaction network. Nature 437(7062):1173–1178. doi: nature04209,  10.1038/nature04209 PubMedCrossRefGoogle Scholar
  54. 54.
    Sam L, Liu Y, Li J, Friedman C, Lussier YA (2007) Discovery of protein interaction networks shared by diseases. Pac Symp Biocomput 76–87Google Scholar
  55. 55.
    Smith NG, Eyre-Walker A (2003) Human disease genes: patterns and predictions. Gene 318:169–175PubMedCrossRefGoogle Scholar
  56. 56.
    Stelzl U, Worm U, Lalowski M, Haenig C, Brembeck FH, Goehler H, Stroedicke M, Zenkner M, Schoenherr A, Koeppen S, Timm J, Mintzlaff S, Abraham C, Bock N, Kietzmann S, Goedde A, Toksoz E, Droege A, Krobitsch S, Korn B, Birchmeier W, Lehrach H, Wanker EE (2005) A human protein-protein interaction network: a resource for annotating the proteome. Cell 122(6):957–968. doi: S0092-8674(05)00866-4,  10.1016/j.cell.2005.08.029 PubMedCrossRefGoogle Scholar
  57. 57.
    Sun PG, Gao L, Han S (2010) Prediction of human disease-related gene clusters by clustering analysis. Int J Biol Sci 7(1):61–73Google Scholar
  58. 58.
    Szklarczyk D, Franceschini A, Kuhn M, Simonovic M, Roth A, Minguez P, Doerks T, Stark M, Muller J, Bork P, Jensen LJ, von Mering C (2011) The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Res 39(Database issue):D561–D568. doi: 10.1093/nar/gkq973, gkq973 [pii]PubMedCrossRefGoogle Scholar
  59. 59.
    Thornblad TA, Elliott KS, Jowett J, Visscher PM (2007) Prioritization of positional candidate genes using multiple web-based software tools. Twin Res Hum Genet 10(6):861–870PubMedCrossRefGoogle Scholar
  60. 60.
    Tiffin N (2011) Conceptual thinking for in silico prioritization of candidate disease genes. Methods Mol Biol 760:175–187. doi: 10.1007/978-1-61779-176-5_11 PubMedGoogle Scholar
  61. 61.
    Tiffin N, Adie E, Turner F, Brunner HG, van Driel MA, Oti M, Lopez-Bigas N, Ouzounis C, Perez-Iratxeta C, Andrade-Navarro MA, Adeyemo A, Patti ME, Semple CA, Hide W (2006) Computational disease gene identification: a concert of methods prioritizes type 2 diabetes and obesity candidate genes. Nucleic Acids Res 34(10):3067–3081PubMedCrossRefGoogle Scholar
  62. 62.
    Tiffin N, Kelso JF, Powell AR, Pan H, Bajic VB, Hide WA (2005) Integration of text- and data-mining using ontologies successfully selects disease gene candidates. Nucleic Acids Res 33(5):1544–1552PubMedCrossRefGoogle Scholar
  63. 63.
    Tranchevent LC, Barriot R, Yu S, Van Vooren S, Van Loo P, Coessens B, De Moor B, Aerts S, Moreau Y (2008) ENDEAVOUR update: a web resource for gene prioritization in multiple species. Nucleic Acids Res 36(Web Server issue):W377–W384PubMedCrossRefGoogle Scholar
  64. 64.
    Tranchevent LC, Capdevila FB, Nitsch D, De Moor B, De Causmaecker P, Moreau Y (2011) A guide to web tools to prioritize candidate genes. Brief Bioinform 12(1):22–32. doi: 10.1093/bib/bbq007, bbq007 [pii]PubMedCrossRefGoogle Scholar
  65. 65.
    Turner FS, Clutterbuck DR, Semple CA (2003) POCUS: mining genomic sequence annotation to predict disease genes. Genome Biol 4(11):R75PubMedCrossRefGoogle Scholar
  66. 66.
    Uetz P, Giot L, Cagney G, Mansfield TA, Judson RS, Knight JR, Lockshon D, Narayan V, Srinivasan M, Pochart P, Qureshi-Emili A, Li Y, Godwin B, Conover D, Kalbfleisch T, Vijayadamodar G, Yang M, Johnston M, Fields S, Rothberg JM (2000) A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae. Nature 403(6770):623–627. doi: 10.1038/35001009 PubMedCrossRefGoogle Scholar
  67. 67.
    van Driel MA, Bruggeman J, Vriend G, Brunner HG, Leunissen JA (2006) A text-mining analysis of the human phenome. Eur J Hum Genet 14(5):535–542PubMedCrossRefGoogle Scholar
  68. 68.
    van Driel MA, Cuelenaere K, Kemmeren PP, Leunissen JA, Brunner HG (2003) A new web-based data mining tool for the identification of candidate genes for human genetic disorders. Eur J Hum Genet 11(1):57–63PubMedCrossRefGoogle Scholar
  69. 69.
    van Driel MA, Cuelenaere K, Kemmeren PP, Leunissen JA, Brunner HG, Vriend G (2005) GeneSeeker: extraction and integration of human disease-related information from web-based genetic databases. Nucleic Acids Res 33(Web Server issue):W758–W761PubMedCrossRefGoogle Scholar
  70. 70.
    Vanunu O, Magger O, Ruppin E, Shlomi T, Sharan R (2010) Associating genes and protein complexes with disease via network propagation. PLoS Comput Biol 6(1):e1000641. doi: 10.1371/journal.pcbi.1000641 PubMedCrossRefGoogle Scholar
  71. 71.
    Wat MJ, Veenma D, Hogue J, Holder AM, Yu Z, Wat JJ, Hanchard N, Shchelochkov OA, Fernandes CJ, Johnson A, Lally KP, Slavotinek A, Danhaive O, Schaible T, Cheung SW, Rauen KA, Tonk VS, Tibboel D, de Klein A, Scott DA (2011) Genomic alterations that contribute to the development of isolated and non-isolated congenital diaphragmatic hernia. J Med Genet 48(5):299–307. doi: 10.1136/jmg.2011.089680, 48/5/299 [pii]PubMedCrossRefGoogle Scholar
  72. 72.
    White S, Smyth P (2003) Algorithms for estimating relative importance in networks. Paper presented at the KDD '03: proceedings of the ninth ACM SIGKDD international conference on knowledge discovery and data miningGoogle Scholar
  73. 73.
    Wu X, Jiang R, Zhang MQ, Li S (2008) Network-based global inference of human disease genes. Mol Syst Biol 4:189. doi: msb200827, [pii]  10.1038/msb.2008.27 PubMedCrossRefGoogle Scholar
  74. 74.
    Xu J, Li Y (2006) Discovering disease-genes by topological features in human protein-protein interaction network. Bioinformatics 22(22):2800–2805. doi: btl467, [pii]  10.1093/bioinformatics/btl467 PubMedCrossRefGoogle Scholar
  75. 75.
    Zhu C, Kushwaha A, Berman K, Jegga AG (2012) A vertex similarity-based framework to discover and rank orphan disease-related genes. BMC Syst Biol 6(Suppl 3):S8. doi: 10.1186/1752-0509-6-S3-S8, 1752-0509-6-S3-S8 [pii]PubMedCrossRefGoogle Scholar
  76. 76.
    Zhu M, Zhao S (2007) Candidate gene identification approach: progress and challenges. Int J Biol Sci 3(7):420–427PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Cheng Zhu
    • 1
    • 2
  • Chao Wu
    • 1
    • 2
  • Bruce J. Aronow
    • 3
    • 2
  • Anil G. Jegga
    • 3
    • 2
  1. 1.Department of Computer Science, College of Engineering and Applied ScienceUniversity of CincinnatiCincinnatiUSA
  2. 2.Division of Biomedical InformaticsCincinnati Children’s Hospital Medical CenterCincinnatiUSA
  3. 3.Department of PediatricsUniversity of Cincinnati College of MedicineCincinnatiUSA

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