A semi-supervised learning approach to predict synthetic genetic interactions by combining functional and topological properties of functional gene network

BMC Bioinformatics

Volumn 11, Issue , 2010, Pages

  You, Zhu Hong ^a,b,c   Yin, Zheng ^c   Han, Kyungsook ^d   Huang, De Shuang ^a   Zhou, Xiaobo ^c  

^a INSTITUTE OF GEOLOGY AND GEOPHYSICS   (China)

^b UNIVERSITY OF SCIENCE AND TECHNOLOGY OF CHINA   (China)

^c WEILL CORNELL MEDICAL COLLEGE   (United States)

^d Inha University   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

COMBINATORIAL EXPLOSION; COMPUTATIONAL APPROACH; COMPUTATIONAL SYSTEMS BIOLOGY; PROTEIN-PROTEIN INTERACTIONS; SEMI-SUPERVISED LEARNING; SEMI-SUPERVISED LEARNING (SSL); SUPERVISED CLASSIFIERS; TOPOLOGICAL PROPERTIES;

CLASSIFICATION (OF INFORMATION); COMPLEX NETWORKS; FORECASTING; GENE EXPRESSION; GRAPHIC METHODS; PROTEINS; SUPERVISED LEARNING; SUPPORT VECTOR MACHINES;

TOPOLOGY;

SACCHAROMYCES CEREVISIAE;

PROTEIN;

ARTICLE; ARTIFICIAL INTELLIGENCE; BIOLOGY; GENE REGULATORY NETWORK; GENETICS; METABOLISM; METHODOLOGY; SACCHAROMYCES CEREVISIAE; SYSTEMS BIOLOGY;

ARTIFICIAL INTELLIGENCE; COMPUTATIONAL BIOLOGY; GENE REGULATORY NETWORKS; PROTEINS; SACCHAROMYCES CEREVISIAE; SYSTEMS BIOLOGY;

EID: 77955106017     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-11-343     Document Type: Article

References (58)

1. Hartman J, Garvik B, Hartwell L. Principles for the buffering of genetic variation. Science 2001, 291(5506):1001-1004. 10.1126/science.291.5506.1001, 11232561.
2. Kelley R, Ideker T. Systematic interpretation of genetic interactions using protein networks. Nature Biotechnology 2005, 23(5):561-566. 10.1038/nbt1096, 2814446, 15877074.
3. Tong AH, Lesage G, Bader GD, Ding H, Xu H, Xin X, Young J, Berriz GF, Brost RL, Chang M, et al. Global mapping of the yeast genetic interaction network. Science 2004, 303(5659):808-813. 10.1126/science.1091317, 14764870.
4. Wong SL, Zhang LV, Tong AH, Li Z, Goldberg DS, King OD, Lesage G, Vidal M, Andrews B, Bussey H, et al. Combining biological networks to predict genetic interactions. Proc Natl Acad Sci USA 2004, 101(44):15682-15687. 10.1073/pnas.0406614101, 524818, 15496468.
5. Zhong W, Sternberg PW. Genome-wide prediction of C. elegans genetic interactions. Science 2006, 311(5766):1481-1484. 10.1126/science.1123287, 16527984.
6. Onami S, Kitano H. Genome-wide prediction of genetic interactions in a metazoan. Bioessays 2006, 28(11):1087-1090. 10.1002/bies.20490, 17041895.
7. Camps-Valls G, Marsheva TVB, Zhou DY. Semi-supervised graph-based hyperspectral image classification. Ieee Transactions on Geoscience and Remote Sensing 2007, 45(10):3044-3054.
8. Zhou D, Bousquet O, Lal TN, Weston J, Olkopf BS. Learning with local and global consistency. Advances in Neural Information Processing Systems 16 2004, 321-328.
9. Lee I, Date SV, Adai AT, Marcotte EM. A probabilistic functional network of yeast genes. Science 2004, 306(5701):1555-1558. 10.1126/science.1099511, 15567862.
10. You ZH, Zhang SW, Li LP. Integration of Genomic and Proteomic Data to Predict Synthetic Genetic Interactions Using Semi-supervised Learning. Emerging Intelligent Computing Technology and Applications: With Aspects of Artificial Intelligence 2009, 5755:635-644.
11. Jansen R, Yu HY, Greenbaum D, Kluger Y, Krogan NJ, Chung SB, Emili A, Snyder M, Greenblatt JF, Gerstein M. A Bayesian networks approach for predicting protein-protein interactions from genomic data. Science 2003, 302(5644):449-453. 10.1126/science.1087361, 14564010.
12. Yamanishi Y, Vert JP, Kanehisa M. Protein network inference from multiple genomic data: a supervised approach. Bioinformatics 2004, 20(Suppl 1):i363-370. 10.1093/bioinformatics/bth910, 15262821.
13. To CC, Vohradsky J. Supervised inference of gene-regulatory networks. Bmc Bioinformatics 2008, 9. 10.1186/1471-2105-9-2, 2266705, 18177495.
14. Zhao XM, Wang Y, Chen LN, Aihara K. Protein domain annotation with integration of heterogeneous information sources. Proteins-Structure Function and Bioinformatics 2008, 72(1):461-473.
15. Zheng H, Wang H, Glass DH. Integration of genomic data for inferring protein complexes from global protein-protein interaction networks. IEEE Trans Syst Man Cybern B Cybern 2008, 38(1):5-16. 10.1109/TSMCB.2007.908912, 18270078.
16. Troyanskaya OG, Dolinski K, Owen AB, Altman RB, Botstein D. A Bayesian framework for combining heterogeneous data sources for gene function prediction (in Saccharomyces cerevisiae). Proc Natl Acad Sci USA 2003, 100(14):8348-8353. 10.1073/pnas.0832373100, 166232, 12826619.
17. Linghu B, Snitkin ES, Holloway DT, Gustafson AM, Xia Y, DeLisi C. High-precision high-coverage functional inference from integrated data sources. Bmc Bioinformatics 2008, 9. 10.1186/1471-2105-9-119, 2292694, 18298847.
18. Lee I, Li Z, Marcotte EM. An improved, bias-reduced probabilistic functional gene network of baker's yeast, Saccharomyces cerevisiae. PLoS ONE 2007, 2(10):e988. 10.1371/journal.pone.0000988, 1991590, 17912365.
19. Zhao XM, Wang Y, Chen L, Aihara K. Protein domain annotation with integration of heterogeneous information sources. Proteins 2008, 72(1):461-473. 10.1002/prot.21943, 18214951.
20. Paladugu SR, Zhao S, Ray A, Raval A. Mining protein networks for synthetic genetic interactions. Bmc Bioinformatics 2008, 9. 10.1186/1471-2105-9-426, 2577120, 18844977.
21. Lezon TR, Banavar JR, Cieplak M, Maritan A, Fedoroff NV. Using the principle of entropy maximization to infer genetic interaction networks from gene expression patterns. Proc Natl Acad Sci USA 2006, 103(50):19033-19038. 10.1073/pnas.0609152103, 1748172, 17138668.
22. Scott BT, Bovill EG, Callas PW, Hasstedt SJ, Leppert MF, Valliere JE, Varvil TS, Long GL. Genetic screening of candidate genes for a prothrombotic interaction with type I protein C deficiency in a large kindred. Thromb Haemost 2001, 85(1):82-87.
23. Damjanovic A, Garcia-Moreno B, Lattman EE, Garcia AE. Molecular dynamics study of hydration of the protein interior. Computer Physics Communications 2005, 169(1-3):126-129.
24. Whitten ST, Garcia-Moreno B, Hilser VJ. Local conformational fluctuations can modulate the coupling between proton binding and global structural transitions in proteins. Proceedings of the National Academy of Sciences of the United States of America 2005, 102(12):4282-4287. 10.1073/pnas.0407499102, 555489, 15767576.
25. Tu K, Yu H, Li YX. Combining gene expression profiles and protein-protein interaction data to infer gene functions. J Biotechnol 2006, 124(3):475-485. 10.1016/j.jbiotec.2006.01.024, 16530869.
26. Segal E, Wang H, Koller D. Discovering molecular pathways from protein interaction and gene expression data. Bioinformatics 2003, 19(Suppl 1):i264-271. 10.1093/bioinformatics/btg1037, 12855469.
27. Tornow S, Mewes HW. Functional modules by relating protein interaction networks and gene expression. Nucleic Acids Res 2003, 31(21):6283-6289. 10.1093/nar/gkg838, 275479, 14576317.
28. Xiao G, Pan W. Gene function prediction by a combined analysis of gene expression data and protein-protein interaction data. J Bioinform Comput Biol 2005, 3(6):1371-1389. 10.1142/S0219720005001612, 16374912.
29. Jansen R, Greenbaum D, Gerstein M. Relating whole-genome expression data with protein-protein interactions. Genome Res 2002, 12(1):37-46. 10.1101/gr.205602, 155252, 11779829.
30. Greenbaum D, Jansen R, Gerstein M. Analysis of mRNA expression and protein abundance data: an approach for the comparison of the enrichment of features in the cellular population of proteins and transcripts. Bioinformatics 2002, 18(4):585-596. 10.1093/bioinformatics/18.4.585, 12016056.
31. Eisen MB, Spellman PT, Brown PO, Botstein D. Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA 1998, 95(25):14863-14868. 10.1073/pnas.95.25.14863, 24541, 9843981.
32. Aittokallio T, Schwikowski B. Graph-based methods for analysing networks in cell biology. Brief Bioinform 2006, 7(3):243-255. 10.1093/bib/bbl022, 16880171.
33. Kafri R, Dahan O, Levy J, Pilpel Y. Preferential protection of protein interaction network hubs in yeast: Evolved functionality of genetic redundancy. Proceedings of the National Academy of Sciences of the United States of America 2008, 105(4):1243-1248. 10.1073/pnas.0711043105, 2234123, 18216251.
34. Lubovac Z, Gamalielsson J, Olsson B. Combining functional and topological properties to identify core modules in Protein Interaction Networks. Proteins-Structure Function and Bioinformatics 2006, 64(4):948-959.
35. Schormann N, Senkovich O, Walker K, Wright DL, Anderson AC, Rosowsky A, Ananthan S, Shinkre B, Velu S, Chattopadhyay D. Structure-based approach to pharmacophore identification, in silico screening, and three-dimensional quantitative structure-activity relationship studies for inhibitors of Trypanosoma cruzi dihydrofolate reductase function. Proteins-Structure Function and Bioinformatics 2008, 73(4):889-901.
36. Caruana R, Niculescu-Mizil A. An Empirical Comparison of Supervised Learning Algorithms. Proceedings of the 23rd international conference on Machine learning 2006, 148:161-168.
37. Stark C, Breitkreutz BJ, Reguly T, Boucher L, Breitkreutz A, Tyers M. BioGRID: a general repository for interaction datasets. Nucleic Acids Res 2006, (34 Database):D535-539. 10.1093/nar/gkj109, 1347471, 16381927.
38. Ho Y, Gruhler A, Heilbut A, Bader GD, Moore L, Adams SL, Millar A, Taylor P, Bennett K, Boutilier K, et al. Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry. Nature 2002, 415(6868):180-183. 10.1038/415180a, 11805837.
39. Gavin AC, Bosche M, Krause R, Grandi P, Marzioch M, Bauer A, Schultz J, Rick JM, Michon AM, Cruciat CM, et al. Functional organization of the yeast proteome by systematic analysis of protein complexes. Nature 2002, 415(6868):141-147. 10.1038/415141a, 11805826.
40. Hakamada K, Hanai T, Honda H, Kobayashi T. Preprocessing method for inferring genetic interaction from gene expression data using Boolean algorithm. J Biosci Bioeng 2004, 98(6):457-463.
41. Cho RJ, Campbell MJ, Winzeler EA, Steinmetz L, Conway A, Wodicka L, Wolfsberg TG, Gabrielian AE, Landsman D, Lockhart DJ, et al. A genome-wide transcriptional analysis of the mitotic cell cycle. Molecular Cell 1998, 2(1):65-73. 10.1016/S1097-2765(00)80114-8, 9702192.
42. Spellman PT, Sherlock G, Zhang MQ, Iyer VR, Anders K, Eisen MB, Brown PO, Botstein D, Futcher B. Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Molecular Biology of the Cell 1998, 9(12):3273-3297. 25624, 9843569.
43. Bhattacharya A, De RK. Divisive Correlation Clustering Algorithm (DCCA) for grouping of genes: detecting varying patterns in expression profiles. Bioinformatics 2008, 24(11):1359-1366. 10.1093/bioinformatics/btn133, 18407922.
44. Maraziotis IA, Dimitrakopoulou K, Bezerianos A. Growing functional modules from a seed protein via integration of protein interaction and gene expression data. Bmc Bioinformatics 2007, 8. 10.1186/1471-2105-8-408, 2233647, 17956603.
45. Barrat A, Barthelemy M, Pastor-Satorras R, Vespignani A. The architecture of complex weighted networks. Proceedings of the National Academy of Sciences of the United States of America 2004, 101(11):3747-3752. 10.1073/pnas.0400087101, 374315, 15007165.
46. Katz L. A New Status Index Derived from Sociometric Analysis. Psychometrika 1953, 18(1):39-43.
47. Dijkstra EW. A note on two problems in connexion with graphs. Numerische Mathematik 1959, 1:269-271.
48. Opsahl T, Panzarasa P. Clustering in weighted networks. Social Networks 2009, 31(2):155-163.
49. Newman MEJ. Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality. Physical Review E 2001, 6401(1).
50. Csardi G, Nepusz T. The igraph software package for complex network research. InterJournal 2006, Complex Systems:1695.
51. Freeman LC. Set of Measures of Centrality Based on Betweenness. Sociometry 1977, 40(1):35-41.
52. Stephenson K, Zelen M. Rethinking Centrality: Methods and Applications. Social Networks 1989, 11:1-37.
53. Brandes U, Fleischer D. Centrality measures based on current flow. Stacs 2005, Proceedings 2005, 3404:533-544.
54. Liu R, Zhou JZ, Liu M. A graph-based semi-supervised learning algorithm for web page classification. ISDA 2006: Sixth International Conference on Intelligent Systems Design and Applications 2006, 2:856-860.
55. Cortes C, Vapnik V. Support-Vector Networks. Mach Learn 1995, 20(3):273-297.
56. Drucker H, Wu DH, Vapnik VN. Support vector machines for spam categorization. Ieee T Neural Networ 1999, 10(5):1048-1054.
57. Pontil M, Verri A. Support Vector Machines for 3 D object recognition. Ieee T Pattern Anal 1998, 20(6):637-646.
58. Chang C-C, Lin C-J. LIBSVM: a library for support vector machines. 2001, , http://www.csie.ntu.edu.tw/~cjlin/libsvm/