Ashokkumar Patel, MD
Clinical Research Informatics, CoGEC Fellow
Case Comprehensive Cancer Center
Ashokkumar Patel, MD
Dr. Patel received his M.D. from St. Matthew's University and received his clinical training in the United Kingdom. His expertise includes extensive knowledge of data mining large clinical data sets and work flow integration with informatics toolsets supporting biorepository activities. He is an editorial board member for Biopreservation and Biobanking and the Journal of Pathology Informatics and has authored several scientific papers as well as textbook chapters on informatics topics.
Bodour Salhia, Ph. D
Assistant Professor, Integrated Cancer Genomics Division
Translational Genomics Research Institute
Bodour Salhia, Ph. D
Jay Bowen, M.S.
Program Manager, Biospecimen Core Resource
The Cancer Genome Atlas (TCGA), The Research Institute at Nationwide Children's Hospital
Jay Bowen, MS
Francine Gachupin, Ph. D
Assistant Professor of Family & Community Center
Assistant Director for the Cancer Disparities Institute
Francine Gachupin, Ph. D
Rehan Akbani, Ph. D
Assistant Professor, Bioinformatics & Comp Biology
MD Anderson Cancer Center TCGA Genome Data Analysis Center
Rehan Akbani, Ph. D
Within TCGA, Dr. Akbani's most noteworthy contributions have been in the area of quality control of TCGA data with a focus on detecting, quantifying and correcting batch effects. Dr. Akbani and Dr. Weinstein became co-chairs of the TCGA Batch Effects Working Group, whose objective was to detect and mitigate sources of batch effects within TCGA. Furthermore, Dr. Akbani headed MD Anderson's GDAC project to create a batch effects website for TCGA, where users can assess and visualize batch effects within TCGA data, and have the ability to download batch effects corrected data.
In addition to batch effects, Dr. Akbani has made noteworthy contributions to TCGA proteomics data being generated by Dr. Gordon Mills' lab at MD Anderson. An RPPA data processing pipeline is being planned for automating the task of processing raw RPPA data to make it ready for downstream analysis. Dr. Akbani has also developed a cluster of clusters algorithm that classifies tumor samples into "super clusters" based on a combination of different data type clusters, such as mRNA clusters, miRNA clusters, copy number clusters, etc.