Machine learning algorithms for studying genome structure and function

Speaker:  Jian Ma – Pittsburgh, PA, United States
Topic(s):  Artificial Intelligence, Machine Learning, Computer Vision, Natural language processing

Abstract

Our understanding on how the human genome is organized and how it regulates complex cellular functions remains primitive. However, new algorithms may facilitate discovery through data integration and computation to uncover fundamental patterns. One of the most exciting and intriguing frontiers in genomics and systems biology is to understand how the human genome is organized in three-dimensions within the cell nucleus. However, the principles underlying high-order genome organization and the functional impact of this organization are still poorly understood. We will introduce some of our recent works in developing new machine learning methods by utilizing high-throughput whole-genome mapping data to study spatial localization of chromosome regions within the cell nucleus in different cellular conditions and also across different mammalian species. Collectively, we hope that these new algorithms will contribute to accelerating our understanding of genome structure and function and their variations that result in phenotypic abnormality in human diseases.

About this Lecture

Number of Slides:  20-40
Duration:  30-60 minutes
Languages Available:  English
Last Updated: 

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