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1 | P a g e Machine Learning Approaches in Promoter Sequence Analysis
 

Summary: 1 | P a g e
Machine Learning Approaches in Promoter Sequence Analysis
N.T. Tung (1)
, E. Yang (2)
, I.P. Androulakis (2,*)
(1)
BIOMAPS Institute for Quantitative Biology, Rutgers University
(2)
Department of Biomedical Engineering, Rutgers University
(*)
corresponding author: yannis@rci.rutgers.edu
Abstract
Gene transcription is one of the main biological processes that govern an organism's response
to external stimuli. Understanding the mechanism of gene regulation offers an avenue with
which to model this response. It has been hypothesized that one of the primary mechanisms for
gene regulation is via transcription factor binding in which a protein (transcription factor) binds to
certain sequences in the genome. Computationally, researchers hope to identify both the
promoter region as well as the sequence motifs which are bound by transcription factors via
analysis of genomic sequences. Machine learning methods make the hypothesis that these
sequences are drawn from some underlying but unknown patterns of base-pairs, and are

  

Source: Androulakis, Ioannis (Yannis) - Biomedical Engineering Department & Department of Chemical and Biochemical Engineering, Rutgers University

 

Collections: Engineering; Biology and Medicine