The center for causal discovery of biomedical knowledge from big data

Journal of the American Medical Informatics Association

Volumn 22, Issue 6, 2015, Pages 1132-1136

  Cooper, Gregory F ^a   Bahar, Ivet ^a   Becich, Michael J ^a   Benos, Panayiotis V ^a   Berg, Jeremy ^a,b   Espino, Jeremy U ^a   Glymour, Clark ^c   Jacobson, Rebecca Crowley ^a   Kienholz, Michelle ^b   Lee, Adrian V ^a   Lu, Xinghua ^a   Scheines, Richard ^c  

^a UNIVERSITY OF PITTSBURGH SCHOOL OF MEDICINE   (United States)

^b UNIVERSITY OF PITTSBURGH MEDICAL CENTER   (United States)

^c CARNEGIE MELLON UNIVERSITY   (United States)

Author keywords

Big Data to knowledge (BD2K); Biomedical knowledge; Biomedical science; Causal discovery; Center of excellence

Indexed keywords

ALGORITHM; ARTICLE; BAYES THEOREM; BIOMEDICINE; BRAIN FUNCTION; CAUSAL MODELING; CLINICAL COMPETENCE; COMPUTER PROGRAM; CONNECTOME; GENE MUTATION; HEALTH CARE ORGANIZATION; HEALTH CARE SYSTEM; HUMAN; KNOWLEDGE BASE; KNOWLEDGE DISCOVERY; NEOPLASM; INFORMATION PROCESSING; MEDICAL RESEARCH; TRANSLATIONAL RESEARCH; UNITED STATES;

ALGORITHMS; BIOMEDICAL RESEARCH; DATASETS AS TOPIC; HUMANS; TRANSLATIONAL MEDICAL RESEARCH; UNITED STATES;

EID: 84954217087     PISSN: 10675027     EISSN: 1527974X     Source Type: Journal    
DOI: 10.1093/jamia/ocv059     Document Type: Article

References (45)

1. Glymour C, Cooper GF, eds. Computation, Causation, and Discovery. Cambridge, MA: MIT Press; 1999.
2. Spirtes P, Glymour C, Scheines R. Causation, Prediction, and Search. Cambridge, MA: MIT Press; 2000.
3. Pearl J. Causality: Models, Reasoning, and Inference. Cambridge, U.K.: Cambridge University Press; 2009.
4. Spirtes P. Introduction to causal inference. J Mach Learn Res. 2010;11: 1643-1662.
5. Illari PM, Russo F, Williamson J, eds. Causality in the Sciences. Oxford, UK: Oxford University Press; 2011.
6. Kalish M, Buhlmann P. Causal structure learning and inference: a selective review. Qual Technol Quant Manag. 2014;11:3-21.
7. Shipley B. Cause and Correlation. Cambridge, UK: Cambridge University Press; 2000.
8. Stekhoven DJ, Moraes I, Sveinbjoernsson G, Hennig L, Maathuis MH, Buehlmann P. Causal stability ranking. Bioinformatics. 2012;28(21):2819-2823.
9. Sachs K, Perez O, Pe'er D, Lauffenburger DA, Nolan GP. Causal protein-signaling networks derived from multiparameter single-cell data. Science. 2005;308:523-529.
10. Chen LS, Emmert-Streib F, Storey JD. Harnessing naturally randomized transcription to infer regulatory relationships among genes. Genome Biol. 2007;8(10):R219.
11. Ramsey JD, Hanson SJ, Glymour C. Multi-subject search correctly identifies causal connections and most causal directions in the DCM models of the Smith et al. simulation study. Neuroimage. 2011;58(3):838-848.
12. Zhang K, Hyvärinen A. Distinguising causes from effects using nonlinear acyclic causal models. JMLR Workshop Conf Proc. 2008;6:157-164.
13. Schadt EE, Lamb J, Yang X, et al. An integrative genomics approach to infer causal associations between gene expression and disease. Nat Genet. 2005;37(7):710-717.
14. Hageman RS, Leduc MS, Korstanje R, Paigen B, Churchill GA. A Bayesian framework for inference of the genotype-phenotype map for segregating populations. Genetics. 2011;187(4):1163-1170.
15. Aliferis CF, Statnikov A, Tsamardinos I, Mani S, Koutsoukos XD. Local causal and Markov blanket induction for causal discovery and feature selection for classification-part II: analysis and extensions. J Mach Learn Res. 2010;11: 235-284.
16. Aliferis CF, Statnikov A, Tsamardinos I, Mani S, Koutsoukos XD. Local causal and Markov blanket induction for causal discovery and feature selection for classification-part I: algorithms and empirical evaluation. J Machine Learning Res. 2010;11:171-234.
17. Rockman MV. Reverse engineering the genotype-phenotype map with natural genetic variation. Nature. 2008;456(7223):738-744.
18. Chen Y, Zhu J, Lum PY, et al. Variations in DNA elucidate molecular networks that cause disease. Nature. 2008;452(7186):429-435.
19. Wilkinson DJ. Bayesian methods in bioinformatics and computational systems biology. Brief Bioinform. 2007;8(2):109-116.
20. Needham CJ, Bradford JR, Bulpitt AJ, Westhead DR. Inference in Bayesian networks. Nat Biotechnol. 2006;24(1):51-53.
21. Basso K, Margolin AA, Stolovitzky G, Klein U, Dalla-Favera R, Califano A. Reverse engineering of regulatory networks in human B cells. Nat Genet. 2005;37(4):382-390.
22. Pournara I, Wernisch L. Reconstruction of gene networks using Bayesian learning and manipulation experiments. Bioinformatics. 2004;20(17):2934-2942.
23. Friedman N. Inferring cellular networks using probabilistic graphical models. Science. 2004;303(5659):799-805.
24. Gagneur J, Stegle O, Zhu C, et al. Genotype-environment interactions reveal causal pathways that mediate genetic effects on phenotype. PLoS Genet. 2013;9(9):e1003803.
25. Pe'er D, Hacohen N. Principles and strategies for developing network models in cancer. Cell. 2011;144(6):864-873.
26. Akavia UD, Litvin O, Kim J, et al. An integrated approach to uncover drivers of cancer. Cell. 2010;143(6):1005-1017.
27. Berndt SI, Gustafsson S, Magi R, et al. Genome-wide meta-analysis identifies 11 new loci for anthropometric traits and provides insights into genetic architecture. Nat Genet. 2013;45(5):501-512.
28. Schwartz SM, Schwartz HT, Horvath S, Schadt E, Lee SI. A systematic approach to multifactorial cardiovascular disease: causal analysis. Arterioscler Thromb Vasc Biol. 2012;32(12):2821-2835.
29. Schadt EE, Bjorkegren JL. NEW: network-enabled wisdom in biology, medicine, and health care. Sci Transl Med. 2012;4(115):115rv1.
30. Schadt EE. Molecular networks as sensors and drivers of common human diseases. Nature. 2009;461(7261):218-223.
31. Le TD, Liu L, Liu B, et al. Inferring microRNA and transcription factor regulatory networks in heterogeneous data. BMC Bioinformatics. 2013;14:92.
32. Wang K, Saito M, Bisikirska BC, et al. Genome-wide identification of posttranslational modulators of transcription factor activity in human B cells. Nat Biotechnol. 2009;27(9):829-839.
33. Dojer N, Gambin A, Mizera A, Wilczynski B, Tiuryn J. Applying dynamic Bayesian networks to perturbed gene expression data. BMC Bioinformatics. 2006;7:249.
34. Stingo FC, Chen YA, Vannucci M, Barrier M, Mirkes PE. A Bayesian graphical modeling approach to microRNA regulatory network inference. Ann Appl Stat. 2010;4(4):2024-2048.
35. Tran LM, Zhang B, Zhang Z, et al. Inferring causal genomic alterations in breast cancer using gene expression data. BMC Syst Biol. 2011;5:121.
36. Cooper GF, Herskovits EH. A Bayesian method for the induction of probabilistic networks from data. Machine Learning. 1992;9:309-347.
37. Spirtes P, Cooper GF. An experiment in causal discovery using a pneumonia database. Proceedings of the International Workshop on Artificial Intelligence and Statistics, 1999, Fort Lauderdale, Florida.
38. Yoo C, Cooper GF. Discovery of causal relationships in a generegulation pathway from a mixture of experimental and observational DNA microarray data. Proceedings of the Pacific Symposium on Biocomputing, 2002, Kauai, Hawaii.
39. Lu S, Jin B, Cowart LA, Lu X. From data towards knowledge: Revealing the architecture of signaling systems by unifying knowledge mining and data mining of systematic perturbation data. PLoS One. 2013;8:1-11.
40. Lu S, Lu X. Integrating genome and functional genomics data to reveal perturbed signaling pathways in ovarian cancers. AMIA Summits Transl Sci Proc. 2012;2012:72-78.
41. The Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumours. Nature. 2012;490:61-70.
42. Zhang Y, Noth I, Garcia JG, Kaminski N. A variant in the promoter of MUC5B and idiopathic pulmonary fibrosis. N Engl J Med. 2011;364(16):1576-1577.
43. Ramsey JD, Hanson SJ, Hanson C, Halchenko YO, Poldrack RA, Glymour C. Six problems for causal inference from fMRI. Neuroimage. 2010;49(2):1545-1558.
44. Mumford J, Ramsey JD. Bayesian networks for fMRI: a primer. Neuroimage. 2014;86:573-582.
45. Bühlmann P, van de Geer S. Statistics for High-Dimensional Data: Methods, Theory and Applications. Springer; 2011.