The center for causal discovery of biomedical knowledge from big data
Open accessAbstract
The Big Data to Knowledge (BD2K) Center for Causal Discovery is developing and disseminating an integrated set of open source tools that support causal modeling and discovery of biomedical knowledge from large and complex biomedical datasets. The Center integrates teams of biomedical and data scientists focused on the refinement of existing and the development of new constraint-based and Bayesian algorithms based on causal Bayesian networks, the optimization of software for efficient operation in a supercomputing environment, and the testing of algorithms and software developed using real data from 3 representative driving biomedical projects: cancer driver mutations, lung disease, and the functional connectome of the human brain. Associated training activities provide both biomedical and data scientists with the knowledge and skills needed to apply and extend these tools. Collaborative activities with the BD2K Consortium further advance causal discovery tools and integrate tools and resources developed by other centers.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
LLMs represent a rapidly emerging research paradigm in diabetic complication prediction.
“Currently, LLMs represent a rapidly emerging research paradigm in diabetic complication prediction, with growing studies exploring their potential lies in processing unstructured clinical text [169], enabling personalized health management via conversational agents [170], and supporting biomedical knowledge discovery [171].”
From Artificial intelligence-powered prediction of diabetic complications: from clinical data to molecular omics (2026) · cites this paper as [171]
Citation [171] is attached to the claim that LLMs support biomedical knowledge discovery. The Introduction describes computational methods for discovering causal knowledge, but it does not identify those methods as LLMs or address diabetic complication prediction. (LLM, conf=0.85)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
An integrative genomics approach to infer causal associations between gene expression and disease
Schadt, E. E., Lamb, J., Yang, X., Zhu, J., Edwards, S., GuhaThakurta, D., Sieberts, S. K., Monks, S., Reitman, M., Zhang, C., et al. (2005) · Nature Genetics · doi:10.1038/ng1589 · Cited by 1,043 · see citing works
Berndt, S. I., Gustafsson, S., Mägi, R., Ganna, A., Wheeler, E., Feitosa, M. F., Justice, A. E., Monda, K. L., Croteau-Chonka, D. C., Day, F. R., et al. (2013) · Nature Genetics · doi:10.1038/ng.2606 · Cited by 646 · see citing works
An integrated approach to uncover drivers of cancer
Akavia, U. D., Litvin, O., Kim, J., Sanchez-Garcia, F., Kotliar, D., Causton, H. C., Pochanard, P., Mozes, E., Garraway, L. A., & Pe'er, D. (2010) · Cell · doi:10.1016/j.cell.2010.11.013 · Cited by 484 · see citing works
Bayesian networks for fMRI: A primer
Mumford, J. A., & Ramsey, J. D. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2013.10.020 · Cited by 132 · see citing works
Inference in Bayesian networks
Needham, C. J., Bradford, J. R., Bulpitt, A. J., & Westhead, D. R. (2006) · Nature Biotechnology · doi:10.1038/nbt0106-51 · Cited by 92 · see citing works
Flagged first, then by citations
Research works printed but not linked: 40
- Glymour C, Cooper GF, eds. Computation, Causation, and Discovery. Cambridge, MA: MIT Press; 1999.
- Spirtes P, Glymour C, Scheines R. Causation, Prediction, and Search. Cambridge, MA: MIT Press; 2000.
- Pearl J. Causality: Models, Reasoning, and Inference. Cambridge, U.K.: Cambridge University Press; 2009.
- Spirtes P. Introduction to causal inference. J Mach Learn Res. 2010;11: 1643-1662.
- Illari PM, Russo F, Williamson J, eds. Causality in the Sciences. Oxford, UK: Oxford University Press; 2011.
- Kalish M, Buhlmann P. Causal structure learning and inference: a selective review. Qual Technol Quant Manag. 2014;11:3-21.
- Shipley B. Cause and Correlation. Cambridge, UK: Cambridge University Press; 2000.
- Stekhoven DJ, Moraes I, Sveinbjoernsson G, Hennig L, Maathuis MH, Buehlmann P. Causal stability ranking. Bioinformatics. 2012;28(21):2819-2823.
- Sachs K, Perez O, Pe'er D, Lauffenburger DA, Nolan GP. Causal protein-sig- naling networks derived from multiparameter single-cell data. Science. 2005;308:523-529.
- Chen LS, Emmert-Streib F, Storey JD. Harnessing naturally randomized transcription to infer regulatory relationships among genes. Genome Biol. 2007;8(10):R219.
- 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.
- Zhang K, Hyva ¨rinen A. Distinguising causes from effects using nonlinear acyclic causal models. JMLR Workshop Conf Proc. 2008;6:157-164.
- 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.
- 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.
- 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.
- Rockman MV. Reverse engineering the genotype-phenotype map with natu- ral genetic variation. Nature. 2008;456(7223):738-744.
- Chen Y, Zhu J, Lum PY, et al. Variations in DNA elucidate molecular net- works that cause disease. Nature. 2008;452(7186):429-435.
- Wilkinson DJ. Bayesian methods in bioinformatics and computational sys- tems biology. Brief Bioinform. 2007;8(2):109-116.
- 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.
- Pournara I, Wernisch L. Reconstruction of gene networks using Bayesian learning and manipulation experiments. Bioinformatics. 2004;20(17):2934- 2942.
- Friedman N. Inferring cellular networks using probabilistic graphical models. Science. 2004;303(5659):799-805.
- 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.
- Pe'er D, Hacohen N. Principles and strategies for developing network mod- els in cancer. Cell. 2011;144(6):864-873.
- Schwartz SM, Schwartz HT, Horvath S, Schadt E, Lee SI. A systematic ap- proach to multifactorial cardiovascular disease: causal analysis. Arterioscler Thromb Vasc Biol. 2012;32(12):2821-2835.
- Schadt EE, Bjorkegren JL. NEW: network-enabled wisdom in biology, medi- cine, and health care. Sci Transl Med. 2012;4(115):115rv1.
- Schadt EE. Molecular networks as sensors and drivers of common human diseases. Nature. 2009;461(7261):218-223.
- Le TD, Liu L, Liu B, et al. Inferring microRNA and transcription factor regula- tory networks in heterogeneous data. BMC Bioinformatics. 2013;14:92.
- Wang K, Saito M, Bisikirska BC, et al. Genome-wide identification of post- translational modulators of transcription factor activity in human B cells. Nat Biotechnol. 2009;27(9):829-839.
- Dojer N, Gambin A, Mizera A, Wilczynski B, Tiuryn J. Applying dynamic Bayesian networks to perturbed gene expression data. BMC Bioinformatics. 2006;7:249.
- 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.
- 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.
- Cooper GF, Herskovits EH. A Bayesian method for the induction of probabil- istic networks from data. Machine Learning. 1992;9:309-347.
- 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.
- Yoo C, Cooper GF. Discovery of causal relationships in a gene- regulation pathway from a mixture of experimental and observational DNA microarray data. Proceedings of the Pacific Symposium on Biocomputing, 2002, Kauai, Hawaii.
- 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.
- Lu S, Lu X. Integrating genome and functional genomics data to reveal per- turbed signaling pathways in ovarian cancers. AMIA Summits Transl Sci Proc. 2012;2012:72-78.
- The Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumours. Nature. 2012;490:61-70.
- 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.
- 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.
- Bu ¨hlmann P, van de Geer S. Statistics for High-Dimensional Data: Methods, Theory and Applications. Springer; 2011.
Cited by
Sorted by citations
Xie, X., Wu, C., Huang, Z., Zhou, Y., Huang, J., Dao, F., Yan, D., Deng, K., Lyu, H., Ma, C., et al. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag083 · Cited by 4 · see citing works
“…a rapidly emerging research paradigm in diabetic complication prediction, with growing studies exploring their potential lies in processing unstructured clinical text [169], enabling personalized health management via conversational agents [170], and supporting biomedical knowledge discovery [171].”
- Authors
- Cooper, G. F., Bahar, I., Becich, M. J., Benos, P. V., Berg, J., Espino, J., Glymour, C., Jacobson, R. C., Kienholz, M., Lee, A. V., Lu, X., Scheines, R., & and the Center for Causal Discovery team
- Affiliations
- University of Pittsburgh (US)
- 2 Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
- 1 Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA
- Carnegie Mellon University (US)
- ORCID
- Venue
- Journal of the American Medical Informatics Association
- Publisher
- Oxford University Press (OUP)
- Published
- 2015-07-02
- Volume
- 22
- Issue
- 6
- Pages
- 1132-1136
- ISSN
- 1067-5027 (ISSN-L)
- 1527-974X
- PMC
- PMC5009908
- OpenAlex
- W2133814768
- Cited by
- 36
- References
- 5
- Citations by year
- 2026: 3 · 2025: 1 · 2024: 1 · 2023: 4 · 2022: 4 · 2021: 3 · 2020: 3 · 2019: 3 · 2018: 4 · 2017: 5 · 2016: 4 · 2012: 1
- FWCI
- 4.25
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of the American Medical Informatics Association (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Human Genome Research Institute (U54HG008540)
- U.S. National Library of Medicine (R01LM012011)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Philosophy and History of Science
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- causal discovery, causal Bayesian networks, constraint-based causal discovery, biomedical knowledge discovery, open source software, high-performance computing, cancer driver mutations, lung disease, interdisciplinary training
- MeSH terms
- Algorithms, Humans, United States, Biomedical Research, Translational Research, Biomedical, Datasets as Topic
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
