ThoughtGallery.org > Five Points Lecture: Marylyn D. Ritchie, PhD

Five Points Lecture: Marylyn D. Ritchie, PhD

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.











When: Tue., Sep. 25, 2018 at 4:00 pm - 6:00 pm
Where: New York Genome Center
101 Sixth Ave.
646-977-7000
Price: Free
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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.

Buy tickets/get more info now