Serdar Bozdag

Department of Computer Science at Marquette University

Lecture Information:
  • January 24, 2020
  • 2:00 PM
  • CASE 241

Speaker Bio

Dr. Serdar Bozdag is an Associate Professor in the Department of Computer
Science at Marquette University. He received his BS degree in Computer
Engineering at Marmara University and Ph.D. degree in Computer Science at
the University of California, Riverside. Prior to joining Marquette
University, Dr. Bozdag was a postdoctoral fellow in National Cancer
Institute at the National Institutes of Health. In 2014, he received the
Way Klingler Young Scholar Award. In 2019, he received the NIH痴
Maximizing Investigators’ Research Award (MIRA). Dr. Bozdag has served as
Program Committee member in several bioinformatics conferences including
ISMB, RECOMB/ISCB Conference on Regulatory & Systems Genomics, Great Lakes
Bioinformatics Conference and ACM-BCB. Dr. Bozdag has served as grant
panelist for NIH and NSF. He is an editorial board member of PLOS ONE and
Cancer Informatics journals.
At Marquette University, Dr. Bozdag leads the bioinformatics lab where his
group痴 research goal is to develop open source integrative computational
tools that perform secondary analysis of publicly available multi-omics
biological, clinical and environmental exposure datasets to infer
context-specific regulatory interactions and modules, and to predict
disease associated genes and patient-specific drug response.

Description

“With the advances in high-throughput technologies in biology, numerous
national and international consortiums have generated a vast amount of
genotype, phenotype, gene expression, and epigenetic data, which have been
made available to the scientific community. Many of these data have not
been analyzed to their full potential and further investigation could
provide opportunities to unravel the biological mechanisms behind disease
initiation and progression. In my group, we are interested in developing
integrative computational tools that analyze these datasets to infer
context-specific regulatory interactions and modules and to predict
disease-associated genes. In this talk, I will talk about two approaches
to integrate high dimensional biological datasets. First, I will introduce
a network propagation-based approach to find cold response-related genes
in rice. Second, I will talk about a computational pipeline to derive
microRNA-gene interactions in cancer.”