Machine learning and systems genomics approaches for multi-omics data

Biomarker Research

Volumn 5, Issue 1, 2017, Pages

  Lin, Eugene ^a,b,c   Lane, Hsien Yuan ^a,d  

^a CHINA MEDICAL UNIVERSITY   (Taiwan)

^b VITA GENOMICS INC   (Taiwan)

^c TickleFish Systems Corporation   (United States)

^d CHINA MEDICAL UNIVERSITY HOSPITAL   (Taiwan)

Author keywords

Genomics; Machine learning; Multi omics; Pharmacogenomics; Single nucleotide polymorphisms; Systems genomics

Indexed keywords

COMPUTER MODEL; COMPUTER NETWORK; CONCATENATION BASED INTEGRATION APPROACH; COPY NUMBER VARIATION; ENSEMBLE CLASSIFIER FRAMEWORK; GENE EXPRESSION; GENE METHYLATION; GENETIC ALGORITHM; GENOMICS; GENOTYPE PHENOTYPE CORRELATION; MACHINE LEARNING; MODEL BASED INTEGRATION APPROACH; PHARMACOGENOMICS; PRIORITY JOURNAL; PROBABILISTIC CAUSAL NETWORK FRAMEWORK; REVIEW; SIMULATION; SUPPORT VECTOR MACHINE; TRANSFORMATION BASED INTEGRATION APPROACH;

EID: 85029626553     PISSN: None     EISSN: 20507771     Source Type: Journal    
DOI: 10.1186/s40364-017-0082-y     Document Type: Review

References (70)

1. Katsanis SH, Javitt J, Hudson K. A case study of personalized medicine. Science. 2008;v320(4):53-4.
2. Snyderman R. Personalized health care: from theory to practice. Biotechnol J. 2012;v7:973-9.
3. Lin E. Novel drug therapies and diagnostics for personalized medicine and nanomedicine in genome science, nanoscience, and molecular engineering. Pharma Regul Aff. 2012;1:e116.
4. Lin E, Lin CG, Wang JY, Wu LS. Gene-gene interactions among genetic variants from seven candidate genes with pediatric asthma in a Taiwanese population. Curr Topics Genet. 2009;3:83-8.
5. Lin E, Hong CJ, Hwang JP, Liou YJ, Yang CH, Cheng D, et al. Gene-gene interactions of the brain-derived neurotrophic-factor and neurotrophic tyrosine kinase receptor 2 genes in geriatric depression. Rejuvenation Res. 2009;12(6):387-93.
6. Lin E, Tsai SJ. Gene-gene interactions in a context of individual variability in antipsychotic drug pharmacogenomics. Curr Pharmacogenomics Person Med. 2011;9:323-31.
7. Lin E, Pei D, Huang YJ, Hsieh CH, Wu LS. Gene-gene interactions among genetic variants from obesity candidate genes for nonobese and obese populations in type 2 diabetes. Genet Test Mol Biomarkers. 2009;13(4):485-93.
8. Wu LS, Hsieh CH, Pei D, Hung YJ, Kuo SW, Lin E. Association and interaction analyses of genetic variants in ADIPOQ, ENPP1, GHSR, PPARgamma and TCF7L2 genes for diabetic nephropathy in a Taiwanese population with type 2 diabetes. Nephrol Dial Transplant. 2009;24(11):3360-6.
9. Wang CH, Ke WS, Lin E. Evaluation of the ENPP1 and PLIN single nucleotide polymorphisms with type 2 diabetes in a Taiwanese population: evidence for replication and gene-gene interaction. J Investig Med. 2012;60(8):1169-73.
10. Lin E, Tsai SJ. Novel diagnostics R&D for public health and personalized medicine in Taiwan: current state, challenges and opportunities. Curr Pharmacogenomics Person Med. 2012;10:239-46.
11. Lin E, Hwang Y, Tzeng CM. A case study of the utility of the HapMap database for pharmacogenomic haplotype analysis in the Taiwanese population. Mol Diagn Ther. 2006;10:367-70.
12. Lin E, Chen PS. Pharmacogenomics with antidepressants in the STAR*D study. Pharmacogenomics. 2008;9:935-46.
13. Lin E, Lane HY. Genome-wide association studies in pharmacogenomics of antidepressants. Pharmacogenomics. 2015;16(5):555-66.
14. Lin E, Tsai SJ. Genome-wide microarray analysis of gene expression profiling in major depression and antidepressant therapy. Prog Neuropsychopharmacol Biol Psychiatry. 2016;64:334-40.
15. Kononenko I. Machine learning for medical diagnosis: history, state of the art and perspective. Artif Intell Med. 2001;23(1):89-109.
16. Lane HY, Tsai GE, Lin E. Assessing gene-gene interactions in pharmacogenomics. Mol Diagn Ther. 2012;16(1):15-27.
17. Landset S, Khoshgoftaar TM, Richter AN, Hasanin T. A survey of open source tools for machine learning with big data in the hadoop ecosystem. J Big Data. 2015;2:24.
18. Lin E, Tsai SJ. Machine learning and predictive algorithms for personalized medicine: from physiology to treatment. In: Turnbull A, editor. Personalized medicine. New York: Nova Science Publishers (in press).
19. Domingos P, Pazzani M. On the optimality of the simple Bayesian classifier under zero-one loss. Mach Learn. 1997;29:103-37.
20. Quinlan JR. C4.5: programs for machine learning. San Francisco: Morgan Kaufmann Publishers; 1993.
21. Kung SY, Hwang JN. Neural networks for intelligent multimedia processing. Proc IEEE. 1998;86:1244-72.
22. Bishop CM. Neural networks for pattern recognition. Oxford: Clarendon; 1995.
23. Rumelhart DE, Hinton GE, William RJ. Learning internal representation by error propagation. In: Parallel distributed processing: explorations in the microstructure of cognition. Cambridge: MIT Press; 1996. p. 318-62.
24. Vapnik V. The nature of statistical learning theory. New York: Springer; 1995.
25. Lloyd SP. Least squares quantization in PCM. IEEE Trans Inform Theory (Special Issue on Quantization). 1982;IT-28:129-37.
26. Altman NS. An introduction to kernel and nearest-neighbor nonparametric regression. Am Stat. 1992;46(3):175-85.
27. Friedman J, Hastie T, Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J Stat Softw. 2010;33(1):1-22.
28. Zou H, Hastie T. Regularization and variable selection via the elastic net. J R Stat Soc Series B Stat Methodol. 2005;67(suppl):301-20.
29. Kessler RC, Warner CH, Ivany C, Petukhova MV, Rose S, Bromet EJ, et al. Predicting suicides after psychiatric hospitalization in US army soldiers. JAMA Psychiatry. 2015;72(1):49-57.
30. Huang LC, Hsu SY, Lin E. A comparison of classification methods for predicting chronic fatigue syndrome based on genetic data. J Transl Med. 2009;7:81.
31. Lin E, Chen PS, Yang YK, Lee IH, Yeh TL, Gean PW, et al. Modeling shortterm antidepressant responsiveness with artificial neural networks. Open Access Bioinform. 2010;2:55-60.
32. Lin E, Tsai SJ. Genetics and suicide. In: Courtet P, editor. Understanding suicide-from diagnosis to personalized treatment. Switzerland: Springer; 2016.
33. Lin E, Hsu SY. Gender differences and pharmacogenomics with antidepressants in depression. In: Hernandez P, Alonso S, editors. Women and Depression. New York: Nova; 2009.
34. Kim W, Kim KS, Lee JE, Noh DY, Kim SW, Jung YS, et al. Development of novel breast cancer recurrence prediction model using support vector machine. J Breast Cancer. 2012;15(2):230-8.
35. Tseng CJ, Lu CJ, Chang CC, Chen GD. Application of machine learning to predict the recurrence-proneness for cervical cancer. Neural Comput & Applic. 2014;24(6):1311-6.
36. Chen YC, Ke WC, Chiu HW. Risk classification of cancer survival using ANN with gene expression data from multiple laboratories. Comput Biol Med. 2014;48:1-7.
37. Chang SW, Abdul-Kareem S, Merican AF, Zain RB. Oral cancer prognosis based on clinicopathologic and genomic markers using a hybrid of feature selection and machine learning methods. BMC Bioinformatics. 2013;14:170.
38. Rosado P, Lequerica-Fernández P, Villallaín L, Peña I, Sanchez-Lasheras F, de Vicente JC. Survival model in oral squamous cell carcinoma based on clinicopathological parameters, molecular markers and support vector machines. Expert Syst Appl. 2013;40(12):4770-6.
39. Lin E, Hwang Y, Wang SC, Gu ZJ, Chen EY. An artificial neural network approach to the drug efficacy of interferon treatments. Pharmacogenomics. 2006;7:1017-24.
40. Ke WS, Hwang Y, Lin E. Pharmacogenomics of drug efficacy in the interferon treatment of chronic hepatitis C using classification algorithms. Adv Appl Bioinform Chem. 2010;3:39-44.
41. Lin E, Hwang Y. A support vector machine approach to assess drug efficacy of interferon-alpha and ribavirin combination therapy. Mol Diagn Ther. 2008;12:219-23.
42. Ritchie MD, Holzinger ER, Li R, Pendergrass SA, Kim D. Methods of integrating data to uncover genotype-phenotype interactions. Nat Rev Genet. 2015; 16(2):85-97.
43. Draghici S, Potter RB. Predicting HIV drug resistance with neural networks. Bioinformatics. 2003;19:98-107.
44. Akavia UD, Litvin O, Kim J, Sanchez-Garcia F, Kotliar D, Causton HC, et al. An integrated approach to uncover drivers of cancer. Cell. 2010;143:1005-17.
45. Zhu J, Sova P, Xu Q, Dombek KM, Xu EY, Vu H, et al. Stitching together multiple data dimensions reveals interacting metabolomic and transcriptomic networks that modulate cell regulation. PLoS Biol. 2012;10:e1001301.
46. Kim D, Li R, Dudek SM, Ritchie MD. ATHENA: identifying interactions between different levels of genomic data associated with cancer clinical outcomes using grammatical evolution neural network. BioData Min. 2013;6:23.
47. Pearl J. Probabilistic reasoning in intelligent systems: networks of plausible inference. San Mateo: Morgan Kaufmann Publishers; 1988.
48. Madigan D, York J. Bayesian graphical models for discrete data. Int Stat Rev. 1995;63:215-32.
49. Schwarz G. Estimating the dimension of a model. Ann Stat. 1978;6:461-4.
50. Shen HB, Chou KC. Ensemble classifier for protein fold pattern recognition. Bioinformatics. 2006;22(14):1717-22.
51. Fridley BL, Lund S, Jenkins GD, Wang LA. Bayesian integrative genomic model for pathway analysis of complex traits. Genet Epidemiol. 2012;36:352-9.
52. Mankoo PK, Shen R, Schultz N, Levine DA, Sander C. Time to recurrence and survival in serous ovarian tumors predicted from integrated genomic profiles. PLoS ONE. 2011;6:e24709.
53. Holzinger ER, Dudek SM, Frase AT, Pendergrass SA, Ritchie MD. ATHENA: the analysis tool for heritable and environmental network associations. Bioinformatics. 2014;30:698-705.
54. Shen R, Mo Q, Schultz N, Seshan VE, Olshen AB, Huse J, et al. Integrative subtype discovery in glioblastoma using iCluster. PLoS ONE. 2012;7:e35236.
55. Kirk P, Griffin JE, Savage RS, Ghahramani Z, Wild DL. Bayesian correlated clustering to integrate multiple datasets. Bioinformatics. 2012;28:3290-7.
56. Lock EF, Dunson DB. Bayesian consensus clustering. Bioinformatics. 2013;29: 2610-6.
57. Lanckriet GRG, De Bie T, Cristianini N, Jordan MI, Noble WS. A statistical framework for genomic data fusion. Bioinformatics. 2004;20:2626-35.
58. Shin H, Lisewski AM, Lichtarge O. Graph sharpening plus graph integration: a synergy that improves protein functional classification. Bioinformatics. 2007;23:3217-24.
59. Wahl S, Vogt S, Stückler F, Krumsiek J, Bartel J, Kacprowski T, et al. Multiomic signature of body weight change: results from a population-based cohort study. BMC Med. 2015;13:48.
60. Krumsiek J, Suhre K, Illig T, Adamski J, Theis FJ. Gaussian graphical modeling reconstructs pathway reactions from high-throughput metabolomics data. BMC Syst Biol. 2011;5:21.
61. Lin E, Huang LC. Identification of significant genes in genomics using Bayesian variable selection methods. Adv Appl Bioinform Chem. 2008;1:13-8.
62. Bush WS, Dudek SM, Ritchie MD. Biofilter: a knowledge-integration system for the multi-locus analysis of genome-wide association studies. Pac Symp Biocomput. 2009;368-379.
63. Hastie T, Tibshirani R, Friedman J. The elements of statistical learning: data mining, inference, and prediction. New York: Springer; 2001.
64. Greene CS, Penrod NM, Kiralis J, Moore JH. Spatially uniform ReliefF (SURF) for computationally efficient filtering of gene-gene interactions. BioData Min. 2009;2:5.
65. Zou H, Hastie T, Tibshirani R. Sparse principal component analysis. J Comput Graph Stat. 2006;15:265-86.
66. Holland JH. Genetic algorithms. Sci Am. 1992;267:66-72.
67. Saeys Y, Inza I, Larrañaga P. A review of feature selection techniques in bioinformatics. Bioinformatics. 2007;23:2507-17.
68. Kohavi R, John GH. Wrappers for feature subset selection. Artif Intell. 1997; 97:273-324.
69. Lin E, Hwang Y, Liang KH, Chen EY. Pattern-recognition techniques with haplotype analysis in pharmacogenomics. Pharmacogenomics. 2007;8(1):75-83.
70. Leung MKK, Delong A, Alipanahi B, Frey BJ. Machine learning in genomic medicine: a review of computational problems and data sets. Proc IEEE. 2016;104(1):176-97.