A Visual Analytics Approach to Exploring Protein Flexibility Subspaces / Visually Mining High Dimensional Environmental Health Data to Identify Risk Factors for Birth Defects

Refreshments will be served. Abstract: Talk 1: A Visual Analytics Approach to Exploring Protein Flexibility Subspaces Authors: Scott Barlowe, Jing Yang,  Donald J. Jacobs, Dennis R. Livesay, Jamal Alsakran, Ye Zhao, Deeptak Verma, and James Mottonen Understanding what causes proteins to change shape and how the resulting shape influences function will expedite the design of more narrowly focused drugs and therapies. Shape alterations are often the result of flexibility changes in a set of localized neighborhoods that may or may not act in concert. Computational models have been developed to predict flexibility changes under varying empir- ical parameters. In this paper, we tackle a significant challenge facing scientists when analyzing outputs of a computational model, namely how to identify, examine, compare, and group interesting neighborhoods of proteins under different parameter sets. This is a difficult task since comparisons over protein subunits that com- prise diverse neighborhoods are often too complex to characterize with a simple metric and too numerous to analyze manually. Here, we present a series of novel visual analytics approaches toward ad- dressing this task. User scenarios illustrate the utility of these ap- proaches and feedback from domain experts confirms their effec- liveness. Talk 2: Visually Mining High Dimensional Environmental Health Data to Identify Risk Factors for Birth Defects Speaker: Chong Zhang, a PhD student of Dr. Jing Yang We have been collaborating with Texas State University-San Marcos, Texas A&M Health Science Center, and Texas Department of State Health Services on an EPA project aiming at identifying risk factors for birth defects from massive environmental heath data.  A visual analytics approach has been recently developed to help analysts identify potential risk factors for birth defects from hundreds of numerical air pollutant variables and  categorical variables depicting the profiles of the parents. Chong will introduce the project, present the visual analytics approach, and discuss open issues in this project.