Workshop: Causal discovery from -omics, clinical, and other biomedical data

When:
November 15, 2018 @ 1:00 pm – 5:00 pm
2018-11-15T13:00:00-04:00
2018-11-15T17:00:00-04:00
Where:
Centro Universitario UPR Río Piedras, Salón Multiusos
Cost:
Free

Presenters:
David Boone, PhD : Assistant Professor, Department of Biomedical Informatics ;Director, Hillman Academy; Director, Computer Science, Biology, and Biomedical Informatics (CoSBBI) Academy; Director, Internship in Biomedical Research, Informatics, and Computer Science (iBRIC), University of Pittsburgh School of Medicine.

Gregory Cooper, MD, PhD: Professor, Department of Biomedical Informatics and of Intelligent Systems; Vice Chairman, Department of Biomedical Informatics, University of Pittsburgh School of Medicine.

Jeremy Espino, MD, MS: Senior Research Scientist; Director of Information Technology and Open Source Software Development; Director of Information Technology, Real-time Outbreak and Disease Surveillance Laboratory; Department of Biomedical Informatics, University of Pittsburgh School of Medicine

Chunhui Cai, PhD: Senior Research Specialist, Department of Biomedical Informatics, University of Pittsburgh School of Medicine

Would you like to obtain additional insights from omics data, after performing initial, standard analyses? Are you interested in integrating different types of omics data with clinical data? Learn how to probe omics data further through causal modeling. In this workshop, you will walk through three causal discovery analyses using breast cancer TCGA RNA sequencing, copy number, exome sequencing, and clinical data while applying open-source software developed by the Pitt and CMU joint Center for Causal Discovery.

After the session you will have the software, skills, and support necessary to begin applying graphical causal modeling and discovery methods to your data of interest.

Map for venue: https://goo.gl/maps/zaEF1LniC5F2

Spaces are limited. If you are interested in attending, please register at https://bit.ly/2PI8U41

Sponsored by
NIH 1R25MD010399 “Increasing Diversity in Interdisciplinary BD2K (IDI-BD2K)”
NIH U54 HG008540-03S1 “Center for Causal Modeling and Discovery of Biomedical Knowledge from Big Data” (supplemental)
NCI-3U54CA096297 “UPR/MDACC Partnership for Excellence in Cancer Research”
NSF DUE-1356474 “Scholarship fund for excellence in Computer Science and Mathematics”

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