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Marylyn D. Ritchie, PhD

4:00 PM — 6:00 PM

University of Pennsylvania

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Marylyn D. Ritchie, PhD


Department of Genetics
Director, Center for Translational Bioinformatics, Institute for Biomedical Informatics (IBI)
Associate Director for Bioinformatics, Institute for Biomedical Informatics (IBI)
Associate Director, Center for Precision Medicine
University of Pennsylvania



Talk Title: Machine Learning Strategies in the Genome and the Phenome – Toward a Better Understanding of Complex Traits


Modern technology has enabled massive data generation; however, tools and software to work with these data in effective ways are limited. Genome science, in particular, has advanced at a tremendous pace during recent years with dramatic innovations in molecular data generation technology, data collection, and a paradigm shift from single lab science to large, collaborative network/consortia science.  Still, the techniques to analyze these data to extract maximal information have not kept pace.  Comprehensive collections of phenotypic data can be used in more integrated ways to better subset or stratify patients based on the totality of his or her health information.  Similar, the availability of multi-omics data continues to increase.  With the complexity of the networks of biological systems, the likelihood that every patient with a given disease has exactly the same underlying genetic architecture is unlikely. Success in understanding the architecture of complex traits will require a multi-pronged approach.   Through applying machine learning to the rich phenotypic data of the EHR, these data can be mined to identify new and interesting patterns of disease expression and relationships.  Machine learning strategies can also be used for meta-dimensional analysis of multiple omics datasets.  We have been exploring machine learning technologies for evaluating both the phenomic and genomic landscape to improve our understanding of complex traits.  These techniques show great promise for the future of precision medicine.


Marylyn D. Ritchie, PhD is a Faculty Pending in Genetics, Director of the Center for Translational Bioinformatics, Associate Director for Bioinformatics in the Institute for Biomedical Informatics at the University of Pennsylvania School of Medicine. Dr. Ritchie is also Associate Director for the Penn Center for Precision Medicine. Dr. Ritchie is a statistical and computational geneticist with a focus on understanding genetic architecture of complex human disease.  She has expertise in developing novel bioinformatics tools for complex analysis of big data in genetics, genomics, and clinical databases, in particular in the area of Pharmacogenomics.  Some of her methods include Multifactor Dimensionality Reduction (MDR), the Analysis Tool for Heritable and Environmental Network Associations (ATHENA), and the Biosoftware suite for annotating/ filtering variants and genomic regions as well as building models of biological relevance for gene-gene interactions and rare-variant burden/dispersion tests.  Dr. Ritchie has over 15 years of experience in the analysis of complex data and has authored over 250 publications.  Dr. Ritchie has received several awards and honors including selection as a Genome Technology Rising Young Investigator in 2006, an Alfred P. Sloan Research Fellow in 2010, a KAVLI Frontiers of Science fellow by the National Academy of Science from 2011-2014, and she was named one of the most highly cited researchers in her field by Thomas Reuters in 2014.  Dr. Ritchie has extensive experience in all aspects of genetic epidemiology and translational bioinformatics as it relates to human genomics.  She also has extensive expertise in dealing with big data and complex analysis including GWAS, next-generation sequencing, data integration of meta-dimensional omics data, Phenome-wide Association Studies (PheWAS), and development of data visualization approaches.


To learn more about Dr. Marylyn Ritchie’s research visit, Google Scholar – Marylyn Ritchie, PhD.


Q&A Moderator:

Nicolas RobineNicolas Robine, PhD

Assistant Director, Computational Biology

New York Genome Center