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Imaging-Omic correlation studies utilizing CPTAC data (September 9, 2019)

Please join us for our next CPTAC Imaging SIG webinar at Monday, Sep 9, 2019 12:00 pm Eastern.  In this session we'll have two speakers who will provide summaries of their work using CPTAC data in imaging-omic correlation studies.

1) Dr. Olivier Gevaert is an assistant professor at Stanford University focusing on developing machine-learning methods for biomedical decision support from multi-scale data. His lab focuses on multi-scale biomedical data fusion primarily in oncology and neuroscience. The lab develops machine learning methods including Bayesian, kernel methods, regularized regression and deep learning to integrate, clinical, molecular and biomedical image data.  His presentation will show an example of how to process proteomic data from CPTAC Phase 2 projects (breast, ovarian and colorectal) with emphasis on how to use, preprocess and subsequently model proteomic data using bioinformatics algorithms. He will show an example of linking protein data to DNA methylation and mRNA gene expression data, and how proteomic data can be integrated with medical image data. 

2) Runyu Hong is a Graduate Assistant/SCBM PhD Student at the Fenyö Lab in the Institute for Systems Genetics at the NYU Langone Health/NYU School of Medicine. He will speak about their project which trained a deep learning model that can distinguish STK11 mutated and wild type pathology slides from CPTAC-LUAD. He will also discuss how they were able to visualize the morphological features correlated with STK11 mutations based on this model.

Connection details:

URL: https://cbiit.webex.com/cbiit/j.php?MTID=m46af7437b801588996cf7f73600c8d9e
Phone: 650-479-3207 
Access code: 736 153 817

Accessing CPTAC data via Jupyter Notebooks (August 6, 2019)

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