Big data: NIH-funded project applies Bayesian statistics to cancer care

rows of binary data

Image: Wikimedia Commons

Texas A&M University statistician Bani K. Mallick has spent the past two decades using Bayesian statistics to develop more efficient algorithms for quantifying, qualifying and classifying big data. Thanks to a new grant from the National Institutes of Health, Mallick now has his sights set on the second-leading cause of death in the United States and one of the world’s biggest data-generating problems to date – cancer.

Mallick will receive $2.3 million during the next five years in support of his proposal, “Graph-Based Bayesian Analysis of Genomics and Proteomics Data.” Working in collaboration with fellow Texas A&M statistician Raymond J. Carroll as well as MD Anderson Cancer Center researchers Veerabhadran Baladandayuthapani and Han Liang, he will develop new statistical models designed to merge two vital informational areas: cancer-related data and analysis.

“The world of bioinformatics and big data are joining together to discover innovative ways to integrate knowledge for cancer treatments,” Mallick said. “This project will create a wide assortment of novel methods for better integrating data across platforms so that we can effectively obtain a much more complete understanding of cancer characteristics and behavior and thereby improve its prevention, prediction and treatment.”

Mallick, a Distinguished Professor and holder of the Susan M. Arseven ’75 Chair in Data Science and Computational Statistics, is considered one of today’s most influential and productive statisticians as a pioneer in Bayesian nonparametric regression and classification research.

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