Prediction of anti-cancer drug response by kernelized multi-task learning

Artificial Intelligence in Medicine

Volumn 73, Issue , 2016, Pages 70-77

  Tan, Mehmet ^a  

^a TOBB UNIVERSITY OF ECONOMICS AND TECHNOLOGY   (Turkey)

Author keywords

Cancer cell lines; Drug response prediction; Gene expression data; Multi task learning

Indexed keywords

ARTIFICIAL INTELLIGENCE; CELL CULTURE; CHEMOTHERAPY; CYTOLOGY; DISEASES; FORECASTING; GENE EXPRESSION; LEARNING ALGORITHMS; LEARNING SYSTEMS; MEAN SQUARE ERROR;

CANCER CELL LINES; DRUG RESPONSE; GENE EXPRESSION DATA; METHODOLOGICAL COMPARISON; MULTITASK LEARNING; NON-LINEAR RELATIONSHIPS; STATE-OF-THE-ART ALGORITHMS; TECHNOLOGICAL ADVANCES;

CELLS;

ANTINEOPLASTIC AGENT;

ARTICLE; CANCER CELL LINE; CELL STRUCTURE; CONTROLLED STUDY; COPY NUMBER VARIATION; DRUG REPOSITIONING; DRUG RESPONSE; GENE EXPRESSION; GENETIC SELECTION; HUMAN; HUMAN CELL; KERNEL METHOD; PRIORITY JOURNAL; ALGORITHM; COMPUTER SIMULATION; DRUG RESISTANCE; FACTUAL DATABASE; FORECASTING; MACHINE LEARNING; NEOPLASM; TUMOR CELL LINE;

ALGORITHMS; ANTINEOPLASTIC AGENTS; CELL LINE, TUMOR; COMPUTER SIMULATION; DATABASES, FACTUAL; DRUG RESISTANCE, NEOPLASM; FORECASTING; HUMANS; MACHINE LEARNING; NEOPLASMS;

EID: 84992126922     PISSN: 09333657     EISSN: 18732860     Source Type: Journal    
DOI: 10.1016/j.artmed.2016.09.004     Document Type: Article

References (43)

1. [1] Statistics National Center for Health, Health, United States. Health, United States, 2014: with special feature on adults aged 55–64, 2015, National Center for Health Statistics, USA.
2. [2] Barretina, J., Caponigro, G., Stransky, N., Venkatesan, K., Margolin, A.A., Kim, S., et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483:7391 (2012), 603–607.
3. [3] Garnett, M.J., Edelman, E.J., Heidorn, S.J., Greenman, C.D., Dastur, A., Lau, K.W., et al. Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature 483 (2012), 570–575.
4. [4] Jang, S., Neto, E.C., Guinney, J., Friend, S.H., Margolin, A.A., Systematic assessment of analytical methods for drug sensitivity prediction from cancer cell line data. Altman, R.B., Dunker, A.K., Hunter, L., Murray, T.A., Klein, T.E., Ritchie, M.D., (eds.) Pacific symposium on biocomputing, 2014, World Scientific Publishing Company, Singapore, 63–74.
5. [5] Neto, E.C., Jang, I.S., Friend, S.H., Margolin, A.A., The stream algorithm: computationally efficient ridge-regression via Bayesian model averaging, and applications to pharmacogenomic prediction of cancer cell line sensitivity. Altman, R.B., Dunker, A.K., Hunter, L., Murray, T.A., Klein, T.E., Ritchie, M.D., (eds.) Pacific symposium on biocomputing, vol. 19, 2014, World Scientific Publishing Company, Singapore, 27–38.
6. [6] Brubaker, D., Difeo, A., Chen, Y., Pearl, T., Zhai, K., Bebek, G., et al. Drug intervention response predictions with paradigm (DIRPP) identifies drug resistant cancer cell lines and pathway mechanisms of resistance. Altman, R.B., Dunker, A.K., Hunter, L., Murray, T.A., Klein, T.E., Ritchie, M.D., (eds.) Pacific symposium on biocomputing, vol. 19, 2014, World Scientific Publishing Company, Singapore, 125–135.
7. [7] Menden, M.P., Iorio, F., Garnett, M., McDermott, U., Benes, C., Ballester, P.J., et al. Machine learning prediction of cancer cell sensitivity to drugs based on genomic and chemical properties. PLOS ONE, 8(4), 2013.
8. [8] Wan, Q., Pal, R., An ensemble based top performing approach for NCI-DREAM drug sensitivity prediction challenge. PLOS ONE, 9(6), 2014, e101183 1.
9. [9] Costello, J.C., Heiser, L.M., Georgii, E., Gönen, M., Menden, M.P., Wang, N.J., et al. A community effort to assess and improve drug sensitivity prediction algorithms. Nat Biotechnol 32:12 (2014), 1–103.
10. [10] Gonen, M., Margolin, A.A., Drug susceptibility prediction against a panel of drugs using kernelized Bayesian multitask learning. Bioinformatics 30:17 (2014), i556–i563 8.
11. [11] Fang, Y., Qin, Y., Zhang, N., Wang, J., Wang, H., Zheng, X., DISIS: prediction of drug response through an iterative sure independence screening. PLOS ONE, 10(3), 2015, e0120408 1.
12. [12] Zhang, N., Wang, H., Fang, Y., Wang, J., Zheng, X., Shirley Liu, X., Predicting anticancer drug responses using a dual-layer integrated cell line-drug network model. PLOS Comput Biol, 11(9), 2015, e1004498 9.
13. [13] Caruana, R., Multitask learning. Mach Learn 28:1 (1997), 41–75.
14. [14] Evgeniou, T., Pontil, M., Regularized multi-task learning. Kohavi, R., Gehrke, J., DuMouchel, W., Ghosh, J., (eds.) International conference on knowledge discovery and data mining, 2004, 109–117.
15. [15] Argyriou, A., Evgeniou, T., Pontil, M., Convex multi-task feature learning. Mach Learn 73:3 (2008), 243–272.
16. [16] Gong, P., Ye, J., Zhang, C., Robust multi-task feature learning. Yang, Q., Agarwal, D., Pei, J., (eds.) International conference on knowledge discovery & data mining, 2012, ACM, New York, NY, USA, 895–903.
17. [17] Pong, T.K., Tseng, P., Ji, S., Ye, J., Trace norm regularization: Reformulations, algorithms, and multi-task learning. SIAM J Optim 20:6 (2010), 3465–3489.
18. [18] Schölkopf, B., Smola, A.J., Learning with kernels. 2002, MIT Press, Cambridge, MA.
19. [19] Pan, S.J., Yang, Q., A survey on transfer learning. IEEE Trans Knowl Data Eng 22:10 (2010), 1345–1359.
20. [20] Huang, J.T., Li, J., Yu, D., Deng, L., Gong, Y., Cross-language knowledge transfer using multilingual deep neural network with shared hidden layers. Ward, R., Deng, L., Krishnamurthy, V., Plataniotis, K., (eds.) International conference on acoustics, speech and signal processing, 2013, IEEE, Washington, DC, USA, 7304–7308.
21. [21] Saenko, K., Kulis, B., Fritz, M., Darrell, T., Adapting visual category models to new domains. Daniilidis, K., Maragos, P., Paragios, N., (eds.) European conference on computer vision, vol. 6314, LNCS, 2010, Springer, Berlin, Heidelberg, 213–226.
22. [22] Niculescu-Mizil, A., Caruana, R., Inductive transfer for Bayesian network structure learning. Meila, M., Shen, X., (eds.) International conference on artificial intelligence and statistics, 2007, Microtome Publishing, Brookline, MA, USA, 339–346.
23. [23] Ma, Y., Luo, G., Zeng, X., Chen, A., Transfer learning for cross-company software defect prediction. Inform Softw Technol 54:3 (2012), 248–256.
24. [24] Deng, Z., Jiang, Y., Choi, K.S., Chung, F.L., Wang, S., Knowledge-leverage-based TSK fuzzy system modeling. IEEE Trans Neural Netw Learn Syst 24:8 (2013), 1200–1212.
25. [25] Jiang, Y., Deng, Z., Chung, F.L., Wang, S., Multi-task TSK fuzzy system modeling using inter-task correlation information. Inform Sci 298 (2015), 512–533.
26. [26] Deng, Z., Choi, K.S., Jiang, Y., Wang, S., Generalized hidden-mapping ridge regression, knowledge-leveraged inductive transfer learning for neural networks, fuzzy systems and kernel methods. IEEE Trans Cybernet 44:12 (2014), 2585–2599.
27. [27] Lu, J., Behbood, V., Hao, P., Zuo, H., Xue, S., Zhang, G., Transfer learning using computational intelligence: a survey. Knowl-Based Syst 80 (2015), 14–23.
28. [28] Yu, S., Krishnapuram, B., Rosales, R., Bharat Rao, R., Bayesian co-training. J Mach Learn Res 12 (2011), 2649–2680.
29. [29] Sun, S., A survey of multi-view machine learning. Neural Comput Appl 23:7-8 (2013), 2031–2038.
30. [30] Fazel, M., Hindi, H., Boyd, S.P., A rank minimization heuristic with application to minimum order system approximation. Wayne Bequette, B., Banda, S., Mestha, L.K., Balakrishnan, S.N., Leitner, J., (eds.) American control conference, 2001, IEEE, Washington, DC, USA, 4734–4739.
31. [31] Nathan, S., Rennie, J.D.M., Jaakkola, T.S., Maximum-margin matrix factorization. Saul, L.K., Weiss, Y., Bottou, L., (eds.) Advances in neural information processing systems, vol. 17, 2005, MIT Press, Cambridge, MA, USA, 1329–1336.
32. [32] Ji, S., Ye, J., An accelerated gradient method for trace norm minimization. Bottou, L., Littman, M., (eds.) 26th annual international conference on machine learning – ICML’09, 2009, Omnipress, Madison, WI, USA, 457–464.
33. [33] Zhou, J., Chen, J., Ye, J., MALSAR: multi-task learning via structural regularization. 2012, Arizona State University.
34. [34] Rappaport, N., Nativ, N., Stelzer, G., Twik, M., Guan-Golan, Y., Iny Stein, T., et al. MalaCards: an integrated compendium for diseases and their annotation. Database, 2013.
35. [35] Amiri, K.I., Horton, L.W., LaFleur, B.J., Sosman, J.A., Richmond, A., Augmenting chemosensitivity of malignant melanoma tumors via proteasome inhibition: Implication for bortezomib (VELCADE, PS-341) as a therapeutic agent for malignant melanoma. Cancer Res 64:14 (2004), 4912–4918.
36. [36] Reuland, S.N., Goldstein, N.B., Partyka, K.A., Smith, S., Luo, Y., Fujita, M., et al. ABT-737 synergizes with Bortezomib to kill melanoma cells. Biol Open 1:2 (2012), 92–100.
37. [37] Selimovic, D., Porzig, B.B.O.W., El-Khattouti, A., Badura, H.E., Ahmad, M., Ghanjati, F., et al. Bortezomib/proteasome inhibitor triggers both apoptosis and autophagy-dependent pathways in melanoma cells. Cell Signall 25:1 (2013), 308–318.
38. [38] Rogalska, A., Marczak, A., Epothilone B induces human ovarian cancer OV-90 cell apoptosis via external pathway. Environ Toxicol Pharmacol 39:2 (2015), 700–712.
39. [39] Bukowska, B., Rogalska, A., Marczak, A., New potential chemotherapy for ovarian cancer – combined therapy with WP 631 and epothilone B. Life Sci 151 (2016), 86–92, 10.1016/j.lfs.2016.02.095.
40. [40] Savage, D.G.S., Rule, A.J., Tighe, M., Garrett, T.J., Oster, M.W., Lee, R.T., et al. Gemcitabine for relapsed or resistant lymphoma. Ann Oncol 11:5 (2000), 595–597.
41. [41] Brugieres, L., Pacquement, H., Le Deley, M.C., Leverger, G., Lutz, P., Paillard, C., et al. Single-drug vinblastine as salvage treatment for refractory or relapsed anaplastic large-cell lymphoma: a report from the French Society of Pediatric Oncology. J Clin Oncol 27:30 (2009), 5056–5061.
42. [42] Otte, C., Safe and interpretable machine learning: a methodological review. Studies in computational intelligence, vol. 445, 2013, 111–122.
43. [43] Chang, B.H.W., Kernel machines are not black boxes-on the interpretability of kernel-based nonparametric models (Ph.D. thesis). 2014, University of Toronto.