Training Workshops for Quantitative Genomics and Machine Learning
This program is provided by Biostatistics, Epidemiology, and Research Design (BERD) in collaboration with the Departments of Biostatistics Environmental Health Sciences at Mailman School of Public Health through the SHARP (Skills for Health And Research Professionals) Training Program.
Overview
Quantitative Genomics Training: 2-day workshop on concepts, methods, and tools for whole-genome and transcriptome analyses in human health studies
Topics include sequence-based association tests (Burden, SKAT and extensions), functional genomic annotations, analysis of genomic variants in human diseases, transcriptome wide association tests (PrediXcan, MetaXcan, and extensions), Mendelian Randomization techniques and colocalization techniques.
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Machine Learning Boot Camp: 2-day workshop on concepts, techniques, and R data analysis methods with Machine Learning applications in biomedical research.
Topics include penalized regression methods (Ridge and Lasso), support vector machines (SVM), decision trees (random forest), predicting survival outcomes (Cox regression/Lasso, survival forests), clustering algorithms, principle component analysis (PCA), and deep learning.
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Eligibility
Investigators at all career stages are welcome to attend, and we particularly encourage trainees and early-stage investigators to participate.
Cost
For information on cost please visit the SHARP website for each individual class.
Cite it, Submit it, Share it!
If your research has benefited from one or more Irving Institute resources, please remember to:
- Cite our CTSA grant, UL1 TR001873, in any relevant publications, abstracts, chapters, and/or posters.
- Submit your publications to PubMed Central (PMC) for compliance with the NIH Public Access Policy.
- Share your research updates with us by sending an email to: irving_institute@cumc.columbia.edu
