The Consortium Activities Component (CAC) of the Center for Causal Modeling and Discovery (CCMD) of Biomedical Knowledge from Big Data will ensure the Pittsburgh Center's participation and integration into all of the BD2K Center Consortium Activities. With highly collaborative leaders in the fields biomedical informatics, computational and systems biology, philosophy, computer science, supercomputing, statistical genetics, cancer research, fMRI, and lung disease, our CCMD team will serve as a broad interface into many disciplines in data science, biomedicine, and beyond. As part of the Consortium, CCMD will contribute to the achievement of the following BD2K goals: 1) Disseminate data and tools developed by the CCMD through the Consortium;2) Extend data- and software-sharing policies and practices through collaboration with other Consortium sites;3) Develop new methods to analyze big data that integrate with other Consortium sites, such as those proposed in Intra-Consortium Project 1 with Harvard;and 4) Use standards-based metadata to describe the data consumed by the tools of CCMD, such as those proposed in Intra-Consortium Project 2 with Stanford. Part of our approach to achieving these goals will be the deployment of a Technical Catalyst whose main responsibility will be to spend time learning with and from the other funded consortium sites. A key responsibility for this individual will be the production of CCMD technical updates describing how CCMD integration and interoperability will be accomplished after careful study during quarterly, rotating site visits of the other funded BD2K Centers. As part of our commitment to the BD2K Consortium, the CCMD will participate in all Consortium meetings and in all of the Consortium subcommittees, such as data sharing, publication, regulatory, evaluation, and others created by the Steering Committee. We will participate in the development of and abide by all policies set by these committees. Through this CCMD component, we will contribute to the change in research culture that BD2K has as its central goal.
CAC will help accomplish the BD2K goals by integrating CCMD tools with other Consortium Centers through participation in Annual Consortium meetings, our Technical Catalyst Program, our Scientific Catalyst Programs and innovative Intra-Consortium projects focused on standards based metadata to promote interoperability and facilitate integration of novel CCMD algorithms ot analyze complex biomedical data sets.
|Huang, Tianzhi; Alvarez, Angel A; Pangeni, Rajendra P et al. (2016) A regulatory circuit of miR-125b/miR-20b and Wnt signalling controls glioblastoma phenotypes through FZD6-modulated pathways. Nat Commun 7:12885|
|Sedgewick, Andrew J; Shi, Ivy; Donovan, Rory M et al. (2016) Learning mixed graphical models with separate sparsity parameters and stability-based model selection. BMC Bioinformatics 17 Suppl 5:175|
|Spagnolo, Daniel M; Gyanchandani, Rekha; Al-Kofahi, Yousef et al. (2016) Pointwise mutual information quantifies intratumor heterogeneity in tissue sections labeled with multiple fluorescent biomarkers. J Pathol Inform 7:47|
|Lu, Songjian; Cai, Chunhui; Yan, Gonghong et al. (2016) Signal-Oriented Pathway Analyses Reveal a Signaling Complex as a Synthetic Lethal Target for p53 Mutations. Cancer Res 76:6785-6794|
|Spirtes, Peter; Zhang, Kun (2016) Causal discovery and inference: concepts and recent methodological advances. Appl Inform (Berl) 3:3|
|BÃ¶hm, Stefanie; Szakal, Barnabas; Herken, Benjamin W et al. (2016) The Budding Yeast Ubiquitin Protease Ubp7 Is a Novel Component Involved in S Phase Progression. J Biol Chem 291:4442-52|
|Strobl, Eric V; Visweswaran, Shyam (2016) Markov Boundary Discovery with Ridge Regularized Linear Models. J Causal Inference 4:31-48|
|Kummerfeld, Erich; Ramsey, Joseph (2016) Causal Clustering for 1-Factor Measurement Models. KDD 2016:1655-1664|
|Lu, Songjian; Mandava, Gunasheil; Yan, Gaibo et al. (2016) An exact algorithm for finding cancer driver somatic genome alterations: the weighted mutually exclusive maximum set cover problem. Algorithms Mol Biol 11:11|
|Plis, Sergey; Danks, David; Yang, Jianyu (2015) Mesochronal Structure Learning. Uncertain Artif Intell 31:|
Showing the most recent 10 out of 23 publications