An extensive analysis of disease-gene associations using network integration and fast kernel-based gene prioritization methods

Artificial Intelligence in Medicine

Volumn 61, Issue 2, 2014, Pages 63-78

  Valentini, Giorgio ^a   Paccanaro, Alberto ^b   Caniza, Horacio ^b   Romero, Alfonso E ^b   Re, Matteo ^a  

^a UNIVERSITY OF MILAN   (Italy)

^b UNIVERSITY OF LONDON   (United Kingdom)

Author keywords

Gene disease prioritization; Heterogeneous data fusion; MeSH descriptors; Network integration; Node label ranking

Indexed keywords

DATA FUSION; GENES; INTEGRAL EQUATIONS; INTEGRATION; LEARNING SYSTEMS; MESH GENERATION; RANDOM PROCESSES;

DESCRIPTORS; HETEROGENEOUS DATA; LABEL RANKINGS; NETWORK INTEGRATION; PRIORITIZATION;

DATA INTEGRATION;

PROTEIN;

ARTICLE; CONTROLLED STUDY; DISEASE ASSOCIATION; GENE; GENE EXPRESSION PROFILING; GENE FUNCTION; GENE INTERACTION; GENE REGULATORY NETWORK; GENETIC ALGORITHM; GENETIC ANALYSIS; GENETIC ASSOCIATION; GENETIC DISORDER; HUMAN; KERNEL METHOD; LEARNING ALGORITHM; MEDICAL SUBJECT HEADINGS; PRIORITY JOURNAL; PROTEIN PROTEIN INTERACTION; SYSTEM ANALYSIS; ALGORITHM; ARTIFICIAL INTELLIGENCE; GENOMICS; PROCEDURES;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; GENE REGULATORY NETWORKS; GENOMICS; HUMANS; MEDICAL SUBJECT HEADINGS;

EID: 84902547630     PISSN: 09333657     EISSN: 18732860     Source Type: Journal    
DOI: 10.1016/j.artmed.2014.03.003     Document Type: Article

References (70)

1. Barabasi A., Gulbahce N., Loscalzo J. Network medicine: a network-based approach to human disease. Nat Rev Genet 2011, 12:56-68.
2. Tiffin N., Andrade-Navarro M., Perez-Iratxeta C. Linking genes to diseases: it's all in the data. Genome Med 2009, 1(8):77.
3. Moreau Y., Tranchevent L. Computational tools for prioritizing candidate genes: boosting disease gene discovery. Nat Rev Genet 2012, 13(8):523-536.
4. Ashburner M., Ball C., Blake J., Botstein D., Butler H., Cherry J., et al. Gene ontology: tool for the unification of biology. Nat Genet 2000, 25(1):25.
5. Ogata H., Goto S., Sato K., Fujibuchi W., Bono H., Kanehisa M. Kegg: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res 1999, 27(1):29-34.
6. Franke L., van Bakel H., Diosdado B., van Belzen M., Wapenaar M., Wijmenga C. Team: a tool for the integration of expression, and linkage and association maps. Eur J Hum Genet 2004, 12:633-638.
7. Bush W., Dudek S., Ritchie M Biofilter: a knowledge-integration system for the multi-locus analysis of genome-wide association studies. Proc of the Pac Symp Biocomput 2009, 368-379. R.B. Altman, A. Dunker, L. Hunter, T. Murray, T. Klein (Eds.).
8. Krallinger M., Valencia A., Hirschman L. Linking genes to literature: text mining, information extraction, and retrieval applications for biology. Genome Biol 2008, 9(S2):S8.
9. Winnenburg R., Wachter T., Plake C., Doms A., Schroeder M. Facts from text: can text mining help to scale-up high-quality manual duration of gene products with ontologies. Brief Bioinform 2008, 9:466-478.
10. Zanzoni A., Soler-Lopez M., Aloy P. A network base approach to human disease. FEBS Lett 2009, 583:1759-1765.
11. Schadt E. Molecular networks as sensors and drivers of common human diseases. Nature 2009, (461):218-223.
12. Baudot A., Gomez-Lopez G., Valencia A. Translational disease interpretation with molecular networks. Genome Biol 2009, 10(221).
13. Vidal M., Cusick M., Barabasi A. Interactome networks and human disease. Cell 2011, 144:986-998.
14. Kowald A., Schmeier S. Data mining in proteomics. Inform Retrieval 2011, 30(696):5-318.
15. Aerts S., Lambrechts D., Maity S., Van Loo P., Coessens B., De Smet F., et al. Gene prioritization through genomic data fusion. Nat Biotechnol 2006, 24(5):537-544.
16. Navlakha S., Kingsford C. The power of protein interaction networks for associating genes with diseases. Bioinformatics 2010, 26(8):1057-1063.
17. Kann M. Protein interactions and disease: computational approaches to uncover the etiology of diseases. Brief Bioinform 2007, 8(5):333-346.
18. Bromberg Y. Chapter 15: disease gene prioritization. PLoS Comput Biol 2013, 9(4):e1002902.
19. Kohler S., Bauer S., Horn D., Robinson P. Walking the interactome for prioritization of candidate disease genes. Am J Hum Genet 2008, 82(4):948-958.
20. Chen J., Bardes E., Aronow B., Jegga A. Toppgene suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Res 2009, 37(Suppl. 2):W305-W311.
21. Perez-Iratxeta C., Bork P., Andrade M. Association of genes to genetically inherited diseases using data mining. Nat Genet 2002, 31:316-319.
22. Piro R., Di Cunto F. Computational approaches to disease-gene prediction: rationale, classification and successes. FEBS J 2012, 279:678-696.
23. Gonçalves J., Francisco A., Moreau Y., Madeira S. Interactogeneous: disease gene prioritization using heterogeneous networks and full topology scores. PLoS One 2012, 7(11):e49634.
24. Freudenberg J., Propping P. A similarity-based method for genome-wide prediction of disease-relevant human genes. Bioinformatics 2002, 18(Suppl. 2):S110-S115.
25. Turner F., Clutterbuck D., Semple C. Pocus: mining genomic sequence annotation to predict disease genes. Genome Biol 2003, 4(11):75.
26. Franke L., Van Bakel H., Fokkens L., De Jong E., Egmont-Petersen M., Wijmenga C. Reconstruction of a functional human gene network, with an application for prioritizing positional candidate genes. Am J Hum Genet 2006, 78(6):1011.
27. Lee I., Blom U., Wang P., Shim J., Marcotte E. Prioritizing candidate disease genes by network-based boosting of genome-wide association data. Genome Res 2011, 21:1109-1121.
28. Nguyen T., Ho T. Detecting disease genes based on semi-supervised learning and protein-protein interaction networks. Artif Intell Med 2012, 54(1):63-71.
29. Schlicker A., Lengauer T., Albrecht M. Improving disease gene prioritization using the semantic similarity of gene ontology terms. Bioinformatics 2010, 26(18):i561-i567.
30. Jiang R., Gan M., He P. Constructing a gene semantic similarity network for the inference of disease genes. BMC Syst Biol 2011, 5(Suppl. 2):S2.
31. Re M., Mesiti M., Valentini G. A fast ranking algorithm for predicting gene functions in biomolecular networks. IEEE/ACM Trans Comput Biol Bioinform 2012, 9(6):1812-1818.
32. Re M., Valentini G. Network-based drug ranking and repositioning with respect to drug bank therapeutic categories. IEEE/ACM Trans Comput Biol Bioinform 2013, 10(6):1359-1371.
33. Liu B., Jiang T., Ma S., Zhao H., Li J., Jiang X., et al. Exploring candidate genes for human brain diseases from a brain-specific network. Biochem Biophys Res Commun 2006, 349:1308-1314.
34. Piro R., Ala U., Molineris I., Grassi E., Bracco C., Perego G., et al. An atlas of tissue-specific conserved coexpression for functional annotation and disease gene prediction. Eur J Hum Genet 2011, 19:1173-1180.
35. Chen Y., Wang W., Zhou Y., Shields R., Chanda S., Elston R., et al. In silico gene prioritization by integrating multiple data sources. PLoS One 2011, 6(6):e21137.
36. Amberger J., Bocchini C., Scott A., Hamosh A. Mckusick's online Mendelian inheritance in man (omim). Nucleic Acids Res 2009, 37:D793-D796.
37. Sayers E., et al. Database resources of the national center for biotechnology information. Nucleic Acids Res 2009, 38:D267-D270.
38. Davis A., Murphy C., Johnson R., Lay J., Lennon-Hopkins K., Saraceni-Richards C., et al. The comparative toxicogenomics database: update 2013. Nucleic Acids Res 2013, 41(D1):D1104-D1114.
39. Yu S., Van Vooren S., Tranchevent L., De Moor B., Moreau Y. Comparison of vocabularies, representations and ranking algorithms for gene prioritization by text mining. Bioinformatics 2008, 24(16):i119-i125.
40. Re M., Valentini G. Cancer module genes ranking using kernelized score functions. BMC Bioinform 2012, 13(S14):S3.
41. Wu G., Feng X., Stein L. A human functional protein interaction network and its application to cancer data analysis. Genome Biol 2010, 11:R53.
42. Segal E., Friedman N., Koller D., Regev A. A module map showing conditional activity of expression modules in cancer. Nat Genet 2004, 36:1090-1098.
43. Chatr-Aryamontri A., Breitkreutz B., Heinicke S., Boucher L., Winter A., Stark C., et al. The biogrid interaction database: 2013 update. Nucleic Acids Res 2012, 41(Database issue):D816-D823.
44. Resnik P. Semantic similarity in a taxonomy: an information-based measure and its application to problems of ambiguity in natural language. J Artif Intell Res 1999, 11:95-130.
45. Yang H., Nepusz T., Paccanaro A. Improving GO semantic similarity measures by exploring the ontology beneath the terms and modelling uncertainty. Bioinformatics 2012, 28(10):1383-1389.
46. De Bie T., Tranchevent L., van Oeffelen L., Moreau Y. Kernel-based data fusion for gene prioritization. Bioinformatics 2007, 23(ISMB/ECCB 2007):i125-i132.
47. Lee J., Gonzalez G. Towards integrative gene prioritization in Alzheimer's disease. Proc of Pacific Symposium on Biocomputing 2011, 4-13. World Scientific Publishing, Kohala Coast, Hawaii, USA. R.B. Altman, A. Dunker, L. Hunter, T. Murray, T. Klein (Eds.).
48. Ray S., Bandyopadhyay S., Pal S. A weighted power framework for integrating multisource information: gene function prediction in yeast. IEEE Trans Biomed Eng 2012, 59(4):1162-1168.
49. Fraser A., Marcotte E. A probabilistic view of gene function. Nat Genet 2004, 36(6):559-564.
50. Khurana E., Chen J., Gerstein M. Interpretation of genomic variants using a unified biological network approach. PLOS Comput Biol 2013, 9(3):e1002886.
51. Martha V., Liu Z., Guo L., Su Z., Ye Y., Fang H., et al. Constructuing a robust protein-protein interaction network by integrating multiple public databases. BMC Bioinform 2011, 12(Suppl 10):S7.
52. Kato T., Kashima H., Sugiyama M. Integration of multiple networks for robust label propagation. Proc of the SIAM International Conference on Data Mining, SDM 2008 2008, 716-726. SIAM, Atlanta, Georgia, USA. R.B. Altman, C. Apte, H. Park, V. Kamath (Eds.).
53. Smola A., Kondor I. Kernel and regularization on graphs. Proc of the Annual Conf on Computational Learning Theory. Lecture Notes in Computer Science 2003, 144-158. Springer. B. Scholkopf, M. Warmuth (Eds.).
54. Deng M., Chen T., Sun F. An integrated probabilistic model for functional prediction of proteins. J Comput Biol 2004, 11:463-475.
55. Mostafavi S., Morris Q. Fast integration of heterogeneous data sources for predicting gene function with limited annotation. Bioinformatics 2010, 26(14):1759-1765.
56. Oliver S. Guilt-by-association goes global. Nature 2000, 403:601-603.
57. Re M., Valentini G Large scale ranking and repositioning of drugs with respect to drugbank therapeutic categories. International Symposium on Bioinformatics Research and Applications (ISBRA 2012); vol. 7292 of Lecture Notes in Computer Science 2012, 225-236. Springer, Dallas, USA. L. Bleris, I. Mandoiu, R. Schwartz, J. Wang (Eds.).
58. Lippert G., Ghahramani Z., Borgwardt K. Gene function prediction form synthetic lethality networks via ranking on demand. Bioinformatics 2010, 26(7):912-918.
59. Scholkopf B., Tsuda K., Vert J. Kernel Methods in Computational Biology 2004, MIT Press, Cambridge, MA.
60. Le D.H., Kwon Y.K. Neighbor-favoring weight reinforcement to improve random walk-based disease gene prioritization. Comput Biol Chem 2013, 44:1-8.
61. Lovasz L. Random Walks on Graphs: a Survey. Combinatorics, Paul Erdos is Eighty 1993, 2:1-46.
62. Arabidopsis Interactome Mapping Consortium Evidence for network evolution in an Arabidopsis interactome map. Science 2011, 333:601-607.
63. Rodriguez J.D., Perez A., Lozano J. Sensitivity analysis of k-fold cross-validation in prediction error estimation. IEEE Trans Pattern Anal Mach Intell 2010, 32(3):569-574.
64. Sifrim A., Propovic D., Tranchevent L., Ardeshirdavani A., Sakai R., Konings P., et al. eXtasy: variant prioritization by genomic data fusion. Nat Methods 2013, 10(11):1083-1084.
65. Allen J.E., Salzberg SL JIGSAW: integration of multiple sources of evidence for gene prediction. Bioinformatics 2005, 21(18):3596-3603.
66. Hwang D., Rust A., Ramsey S., Smith J., Leslie D., Weston A., et al. A data integration methodology for systems biology. Proc Natl Acad Sci U S A 2005, 102(48):17296-17301.
67. Re M., Valentini G. Simple ensemble methods are competitive with state-of-the-art data integration methods for gene function prediction. J Mach Learn Res 2010, 8:98-111.
68. Cesa-Bianchi N., Re M., Valentini G. Synergy of multi-label hierarchical ensembles, data fusion, and cost-sensitive methods for gene functional inference. Mach Learn 2012, 88(1):209-241.
69. Li Y., Patra J. Integration of multiple data sources to prioritize candidate genes using discounted rating systems. BMC Bioinform 2010, 11(Suppl. I):S20.
70. Re M., Valentini G Ensemble methods: a review. EBT Advances in Machine Learning and Data Mining for Astronomy. Data Mining and Knowledge Discovery Chapman-Hall 2012, 563-594. Chapman-Hall.