Our current understanding of how genes are regulated is akin to solving a jigsaw puzzle. Many factors governing gene expression have been identified, and researchers have collected a wide variety of related datasets. However, how these "parts" are pieced together to function as a whole remains unclear. In this talk, I will discuss our research to develop and apply state-of-the-art machine learning methods to genomics datasets to attempt to put together the pieces from the data. I will cover our work using deep learning architecture that captures the data's underlying structure to integrate datasets and connect them to gene expression via the prediction task. We also interpret the prediction results and tie them back to contributing factors to develop potential hypotheses related to gene regulation. I will then move from bulk data to the single-cell data domain and discuss methods to perform unsupervised integration of different types of single-cell experiments. This talk aims to highlight our research direction's potential to reveal the important gene regulatory mechanisms in characterizing cell types and diseases from the collected data.
Ritambhara Singh is an Assistant Professor in the Computer Science department and a faculty member of the Center for Computational Molecular Biology at Brown University. Her research lab works at the intersection of machine learning and biology. Prior to joining Brown, Singh was a post-doctoral researcher in the Noble Lab at the University of Washington. She completed her Ph.D. in 2018 from the University of Virginia with Dr. Yanjun Qi as her advisor. Her research has involved developing machine learning algorithms for the analysis of biological data as well as applying deep learning models to novel biological and biomedical applications. She recently received the NHGRI Genomic Innovator Award for developing deep learning methods to integrate and model genomics datasets. URL: https://vivo.brown.edu/display/rsingh47