Invited Keynote Lecture: Prof. Mary Yang

by admin last modified Jul 05, 2017 04:38 PM

Mary Yang

Developing Streamlined Computational Intelligence Approaches That Facilitate Single-Cell Genomics Research
Prof. Mary Yang
Department of Genetics, Yale University
New Haven, Connecticut, USA. mary.yang@yale.edu
Director of MidSouth Bioinformatics Center
Director of Joint UALR-UAMS Bioinformatics Ph.D. Program, Little Rock. Arkansas, USA
Date & Time: July 17 (Monday), 2017; 5:20pm - 6:00pm
Location: Ballroom 8

Description

Advances in single-cell genomics research have provided unprecedented opportunities in biomedical research, especially, cancer, where it is important to identify different cell types/populations pertinent to aging and cellular malignancy. Newer and better methods for conducting single-cell analyses are currently available that can capture cell population heterogeneity, including cancer’s heterogeneous nature, and facilitate discovery of the underlying molecular mechanisms. Single-cell RNA sequencing (scRNA-seq) provides an increased resolution of the gene expression, which allows us to investigate the dynamic changes and variation between cells of interest. Identifying different cell types/populations, however important in biomedical research, but along with the high variability in gene expression makes it extremely difficult to distinguish between novel cell types or detect variation.

Although scRNA-seq data analysis allows us to capture the heterogeneous nature of cells and investigate the genes that express differentially across biological conditions, it is a very challenging process to perform the analysis. For instance, first and foremost, variation in gene expression can result as much from technical factors as it can from biological ones. Due to low capture efficiency and stochastic gene expression, gene dropout events are enriched in scRNA-seq data. Addressing this problem requires new approaches in dealing with missing data as well as problems associated with high-dimensional sparse matrix computations, which can be time-consuming, less accurate, and even affected by “curve of dimension.”

In this invited keynote lecture, we will present several newly developed computational approaches in collaboration with Dr. Sherman Morton Weissman, member of National Academy of Sciences and Sterling Professor at Yale University. Due to scRNA-seq data-specific properties, such as the abundance of zeros in gene expression measurements and high variability, novel cell-type detection for disease studies using machine learning models will be presented. We will illustrate our new ideas about developing highly reliable and effective computational methods to address these challenges.

To fully exploit scRNA-seq data, we will present our newly developed machine learning models that can handle missing data and high sparse matrix computation with more classification accuracy and better explanatory power. Our studies aim to facilitate biomedical research, using large-scale single-cell data.

Biography

Dr. Mary Yang was recruited to Arkansas in 2013 to fill the positions as Director of the MidSouth Bioinformatics Center, and Director of the Joint Bioinformatics MS/Ph.D. program in the George Washington Donaghey College of Engineering and Information Technology (EIT) at the University of Arkansas at Little Rock and University of Arkansas for Medical Sciences. Dr. Yang’s Systems Genomics Laboratory has been supported by NIH, FDA, ASTA and Arkansas Research Alliance. She  has served on the Steering Committee of NIH funded AR INBRE as bioinformatics core director for the NIH funded research network in the MidSouth region, and the Review Committee for United States National Science Foundation (NSF) the Advances in Biological Informatics (ABI). She has been the recipient of the NIH Fellows Award for Research Excellence (FARE), the NIH Academic Research Enhancement Award, the Purdue Research Foundation (PRF) Fellowship, IEEE and ISIBM Bioinformatics and Bioengineering Outstanding Achievement Awards, and Basic Science Research Award of Arkansas Science and Technology Authority (ASTA).She is currently on the editorial broads of Journal of Supercomputing (Springer-Nature) and International Journal of Pattern Recognition and Artificial Intelligence (World Scientific). Dr. Yang received M.S.E.C.E. (Computer Engineering), M.S. (Biological Physics), and Ph.D. degrees at Purdue University, supported by an interdisciplinary Bilsland Dissertation Fellowship award. Dr. Yang joined the National Human Genome Research Institute at the National Institute of Health (NIH) in 2005. During her tenure there, she made contributions to large-scale genomics and systems biology research projects, and was Founding Editor-in-Chief of International Journal of Computational Biology and Drug Design, a NIH PubMed indexed journal. Her main research interest is in developing functional genomics and systems biology-based approaches that render a better understanding of the molecular mechanisms underlying complex diseases, such as cancer. Dr. Yang has published over 50 PubMed-indexed biomedical articles and over 70 DBLP-indexed computer science papers. Dr. Yang is currently a Visiting Associate Professor of Genetics at Yale University in New Haven, Connecticut and is a tenured Associate Professor and Director of Bioinformatics at the University of Arkansas in Little Rock, Arkansas, U.S.A.  http://www.mqyang.net.

 


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