The center for causal discovery of biomedical knowledge from big data

被引:23
|
作者
Cooper, Gregory F. [1 ,2 ]
Bahar, Ivet [3 ]
Becich, Michael J. [1 ,2 ]
Benos, Panayiotis V. [3 ]
Berg, Jeremy [3 ]
Espino, Jeremy U. [1 ,4 ,5 ]
Glymour, Clark [6 ]
Jacobson, Rebecca Crowley [1 ,2 ]
Kienholz, Michelle [4 ]
Lee, Adrian V. [7 ]
Lu, Xinghua [1 ,2 ]
Scheines, Richard [8 ]
机构
[1] Univ Pittsburgh, Dept Biomed Informat, Off Baum, Suite 524,5607 Baum Blvd, Pittsburgh, PA 15206 USA
[2] Univ Pittsburgh, Dept Biomed Informat, Pittsburgh, PA USA
[3] Univ Pittsburgh, Dept Computat & Syst Biol, Pittsburgh, PA USA
[4] Univ Pittsburgh, Inst Personalized Med, Pittsburgh, PA USA
[5] UPMC, Pittsburgh, PA USA
[6] Carnegie Mellon Univ, Dept Philosophy, Pittsburgh, PA 15213 USA
[7] Univ Pittsburgh, Dept Pharmacol & Chem Biol, Pittsburgh, PA USA
[8] Carnegie Mellon Univ, Dietrich Coll Humanities & Social Sci, Pittsburgh, PA 15213 USA
基金
美国国家科学基金会;
关键词
Big Data to knowledge (BD2K); center of excellence; causal discovery; biomedical knowledge; biomedical science; MARKOV BLANKET INDUCTION; GENE-EXPRESSION; REGULATORY NETWORKS; MOLECULAR NETWORKS; BAYESIAN NETWORKS; FEATURE-SELECTION; LOCAL CAUSAL; TRANSCRIPTION; INFERENCE; MICRORNA;
D O I
10.1093/jamia/ocv059
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:1132 / 1136
页数:5
相关论文
共 50 条
  • [2] Knowledge Discovery Using Big Data in Biomedical Systems
    Janga, Sarath Chandra
    Zhu, Dongxiao
    Chen, Jake Y.
    Zaki, Mohammed J.
    [J]. IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, 2015, 12 (04) : 726 - 728
  • [3] From biomedical big data to knowledge and action
    Mei Chen
    [J]. Machine Vision and Applications, 2018, 29 : 1209 - 1210
  • [4] From biomedical big data to knowledge and action
    Chen, Mei
    [J]. MACHINE VISION AND APPLICATIONS, 2018, 29 (08) : 1209 - 1210
  • [5] Active sampling for knowledge discovery from biomedical data
    Veeramachaneni, S
    Demichelis, R
    Olivetti, E
    Avesani, P
    [J]. KNOWLEDGE DISCOVERY IN DATABASES: PKDD 2005, 2005, 3721 : 343 - 354
  • [6] Big Data knowledge discovery
    Xhafa, Fatos
    Taniar, David
    [J]. KNOWLEDGE-BASED SYSTEMS, 2015, 79 : 1 - 2
  • [7] Big data analytics and knowledge discovery
    Bellatreche, Ladjel
    Mohania, Mukesh
    [J]. CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, 2016, 28 (15): : 3945 - 3947
  • [8] Towards Knowledge Discovery in Big Data
    Lomotey, Richard K.
    Deters, Ralph
    [J]. 2014 IEEE 8TH INTERNATIONAL SYMPOSIUM ON SERVICE ORIENTED SYSTEM ENGINEERING (SOSE), 2014, : 181 - 191
  • [9] Big Data Analytics and Knowledge Discovery
    Golfarelli, Matteo
    Wrembel, Robert
    [J]. DATA & KNOWLEDGE ENGINEERING, 2023, 146
  • [10] Visualization and Visual Knowledge Discovery from Big Uncertain Data
    Leung, Carson K.
    Madill, Evan W. R.
    Pazdor, Adam
    [J]. 2022 26TH INTERNATIONAL CONFERENCE INFORMATION VISUALISATION (IV), 2022, : 330 - 335