Integrative Data Analysis of Multi-Platform Cancer Data with a Multimodal Deep Learning Approach

IEEE/ACM Transactions on Computational Biology and Bioinformatics

Volumn 12, Issue 4, 2015, Pages 928-937

  Liang, Muxuan ^a,b   Li, Zhizhong ^c   Chen, Ting ^a,d   Zeng, Jianyang ^a  

^a TSINGHUA UNIVERSITY   (China)

^b University of Wisconsin Madison   (United States)

^c GENOMICS INSTITUTE OF THE NOVARTIS RESEARCH FOUNDATION   (United States)

^d UNIVERSITY OF SOUTHERN CALIFORNIA   (United States)

Author keywords

clinical data; genomic data; identification of cancer subtypes; Multi platform cancer data analysis; multimodal deep belief network; restricted Boltzmann machine

Indexed keywords

ALKYLATION; ARTIFICIAL INTELLIGENCE; COMPLEX NETWORKS; DATA HANDLING; DISEASE CONTROL; GENE ENCODING; GENE EXPRESSION; GENES; INFORMATION ANALYSIS; LEARNING ALGORITHMS; LEARNING SYSTEMS; RNA;

CANCER SUBTYPES; CLINICAL DATA; DEEP BELIEF NETWORKS; GENOMIC DATA; MULTI-PLATFORM; RESTRICTED BOLTZMANN MACHINE;

DISEASES;

MICRORNA;

BIOLOGY; GENE EXPRESSION PROFILING; GENETICS; HUMAN; KAPLAN MEIER METHOD; MACHINE LEARNING; METABOLISM; MORTALITY; NEOPLASM; PROCEDURES;

COMPUTATIONAL BIOLOGY; GENE EXPRESSION PROFILING; HUMANS; KAPLAN-MEIER ESTIMATE; MACHINE LEARNING; MICRORNAS; NEOPLASMS;

EID: 84939178155     PISSN: 15455963     EISSN: None     Source Type: Journal    
DOI: 10.1109/TCBB.2014.2377729     Document Type: Article

References (30)

1. R. Shen, A. B. Olshen, and M. Ladanyi, "Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis," Bioinformatics, vol. 25, no. 22, pp. 2906-2912, 2009.
2. S. Zhang, C.-C. Liu, W. Li, H. Shen, P. W. Laird, and X. J. Zhou, "Discovery of multi-dimensional modules by integrative analysis of cancer genomic data," Nucleic Acids Res. , vol. 40, no. 19, pp. 9379-9391, 2012.
3. J. A. Hartigan and M. A. Wong, "Algorithm as 136: A k-means clustering algorithm," J. Royal Statist. Soc. Ser. C (Appl. Statist. ), vol. 28, no. 1, pp. 100-108, 1979.
4. C. Ding and X. He, "K-means clustering via principal component analysis," in Proc. 21st Int. Conf. Mach. Learn. , 2004, p. 29.
5. L. J. van't Veer, H. Dai, M. J. van de Vijver, Y. D. He, A. A. Hart, M. Mao, H. L. Peterse, K. van der Kooy, M. J. Marton, A. T. Witteveen, G. J. Schreiber, R. M. Kerkhoven, C. Roberts, P. S. Linsley, R. Bernards, and S. H. Friend, "Gene expression profiling predicts clinical outcome of breast cancer," Nature, vol. 415, no. 6871, pp. 530-536, 2002.
6. D. R. Rhodes, T. R. Barrette, M. A. Rubin, D. Ghosh, and A. M. Chinnaiyan, "Meta-analysis of microarrays: Interstudy validation of gene expression profiles reveals pathway dysregulation in prostate cancer," Cancer Res. , vol. 62, no. 15, pp. 4427-4433, 2002.
7. M. Kim, J. Nam, H. Lee, J. Ngiam, A. Khosla, and A. Y. Ng, "Multimodal deep learning," in Proc. 28th Int. Conf. Mach. Learn. , 2011, pp. 689-696.
8. N. Srivastava and R. Salakhutdinov, "Multimodal learning with deep boltzmann machines," J. Mach. Learn. Res. , vol. 15, pp. 2949-2980, 2014.
9. G. E. Hinton, "A practical guide to training restricted boltzmann machines," Dept. of Computer Science, Univ. of Toronto, Tech. Rep. UTML TR 2010-003, 2010.
10. R. Salakhutdinov, A. Mnih, and G. Hinton, "Restricted boltzmann machines for collaborative filtering," in Proc. 24th Int. Conf. Mach. Learn. , 2007, pp. 791-798.
11. G. E. Hinton, S. Osindero, and Y.-W. Teh, "A fast learning algorithm for deep belief nets," Neural Comput. , vol. 18, no. 7, pp. 1527-1554, 2006.
12. M. Welling and G. Hinton, "A new learning algorithm for mean field boltzmann machines," in Proc. Int. Conf. Artificial Neural Netw. , 2002, pp. 351-357.
13. M. A. Carreira-Perpinan and G. E. Hinton, "On contrastive divergence learning," in Proc. 10th Int. Workshop Artif. Intell. Statist. , 2005, pp. 59-66.
14. T. Tieleman, "Training restricted boltzmann machines using approximations to the likelihood gradient," in Proc. 25th Int. Conf. Mach. Learn. , 2008, pp. 1064-1071.
15. M. J. Van de Vijver, Y. D. He, L. J. van't Veer, H. Dai, A. A. Hart, D. W. Voskuil, G. J. Schreiber, J. L. Peterse, C. Roberts, M. J. Marton, M. Parrish, D. Atsma, A. Witteveen, A. Glas, L. Delahaye, T. van der Velde, H. Bartelink, S. Rodenhuis, E. T. Rutgers, S. H. Friend, and R. Bernards, "A gene-expression signature as a predictor of survival in breast cancer," New England J. Med. , vol. 347, no. 25, pp. 1999-2009, 2002.
16. K.-J. Wu, C. Grandori, M. Amacker, N. Simon-Vermot, A. Polack, J. Lingner, and R. Dalla-Favera, "Direct activation of tert transcription by c-myc," Nat. Genetics, vol. 21, no. 2, pp. 220-224, 1999.
17. F. W. Huang, E. Hodis, M. J. Xu, G. V. Kryukov, L. Chin, and L. A. Garraway, "Highly recurrent tert promoter mutations in human melanoma," Science, vol. 339, no. 6122, pp. 957-959, 2013.
18. A. Glukhov, L. Svinareva, S. Severin, and V. Shvets, "Telomerase inhibitors as novel antitumor drugs," Appl. Biochem. Microbiol. , vol. 47, no. 7, pp. 655-660, 2011.
19. S.-Y. Park, J. H. Lee, M. Ha, J.-W. Nam, and V. N. Kim, "mir-29 mirnas activate p53 by targeting p85a and cdc42," Nat. Struct. Molecular Biol. , vol. 16, no. 1, pp. 23-29, 2008.
20. H. J. Bae, J. H. Noh, J. K. Kim, J. W. Eun, K. H. Jung, M. G. Kim, Y. G. Chang, Q. Shen, S.-J. Kim, W. S. Park, J. Y. Lee, and S. W. Nam, "Microrna-29c functions as a tumor suppressor by direct targeting oncogenic sirt1 in hepatocellular carcinoma," Oncogene, vol. 33, no. 20, pp. 2557-2567, 2013.
21. J.-J. Zhao, J. Lin, T. Lwin, H. Yang, J. Guo, W. Kong, S. Dessureault, L. C. Moscinski, D. Rezania, W. S. Dalton, E. Sotomayor, J. Tao, and J. Q. Cheng, "Microrna expression profile and identification of mir-29 as a prognostic marker and pathogenetic factor by targeting cdk6 in mantle cell lymphoma," Blood, vol. 115, no. 13, pp. 2630-2639, 2010.
22. W. L. McGuire and C. K. Osborne, "The use of steroid hormone receptors in the treatment of human breast cancer: A review," Bull Cancer, vol. 66, no. 3, pp. 203-209, 1979.
23. J. Underwood, "Oestrogen receptors in human breast cancer: Review of histopathological correlations and critique of histochemical methods," Diagnostic Histopathol. , vol. 6, no. 1, pp. 1-22, 1983.
24. R. Salakhutdinov and G. E. Hinton, "Deep boltzmann machines," in Proc. Int. Conf. Artif. Intell. Statist. , 2009, pp. 448-455.
25. A. Mohamed, T. Sainath, G. Dahl, B. Ramabhadran, G. Hinton, and M. Picheny, "Deep belief networks using discriminative features for phone recognition," in Proc. IEEE Int. Conf. Acoust. , Speech Signal Process. , 2011, pp. 5060-5063.
26. G. Hinton, L. Deng, D. Yu, G. E. Dahl, A.-r. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, and T. N. Sainath, "Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups," IEEE Signal Process. Mag. , vol. 29, no. 6, pp. 82-97, Nov. 2012.
27. B. T. Sherman, D. W. Huang, and R. A. Lempicki, "Systematic and integrative analysis of large gene lists using david bioinformatics resources," Nat. Protocols, vol. 4, no. 1, pp. 44-57, 2008.
28. W. Huang da, B. T. Sherman, and R. A. Lempicki, "Bioinformatics enrichment tools: Paths toward the comprehensive functional analysis of large gene lists," Nucleic Acids Res. , vol. 37, no. 1, pp. 1-13, 2009.
29. N. Le Roux and Y. Bengio, "Representational power of restricted boltzmann machines and deep belief networks," Neural Comput. , vol. 20, no. 6, pp. 1631-1649, 2008.
30. Y. Wang and J. Zeng, "Predicting drug-target interactions using restricted boltzmann machines," Bioinformatics, vol. 29, no. 13, pp. i126-i134, 2013.