Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1093/jamia/ocv059 | JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION |
Keywords | Field | DocType |
Big Data to knowledge (BD2K),center of excellence,causal discovery,biomedical knowledge,biomedical science | Data science,Data mining,Connectome,Computer science,Knowledge management,Datasets as Topic,Bayesian network,Software,Dissemination,Big data,Causal model,Bayesian probability | Journal |
Volume | Issue | ISSN |
22 | 6 | 1067-5027 |
Citations | PageRank | References |
7 | 0.54 | 13 |
Authors | ||
12 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gregory F. Cooper | 1 | 3464 | 580.16 |
| Ivet Bahar | 2 | 361 | 39.41 |
| Michael J. Becich | 3 | 50 | 9.32 |
| Panayiotis V. Benos | 4 | 253 | 19.89 |
| Jeremy M. Berg | 5 | 8 | 1.26 |
| Jeremy U. Espino | 6 | 73 | 36.48 |
| Clark Glymour | 7 | 468 | 82.20 |
| rebecca crowley jacobson | 8 | 7 | 0.54 |
| michelle l kienholz | 9 | 7 | 0.54 |
| Adrian V Lee | 10 | 13 | 1.59 |
| Xinghua Lu | 11 | 233 | 16.94 |
| Richard Scheines | 12 | 256 | 37.19 |