Biology
Document Type
Article
Abstract
Understanding the molecular machinery involved in transcriptional regulation is central to improving our knowledge of an organism’s development, disease, and evolution. The building blocks of this complex molecular machinery are an organism’s genomic DNA sequence and transcription factor proteins. Despite the vast amount of sequence data now available for many model organisms, predicting where transcription factors bind, often referred to as ‘motif detection’ is still incredibly challenging. In this study, we develop a novel bioinformatic approach to binding site prediction. We do this by extending pre-existing SVM approaches in an unbiased way to include all possible gapped k-mers, representing different combinations of complex nucleotide dependencies within binding sites. We show the advantages of this new approach when compared to existing SVM approaches, through a rigorous set of cross-validation experiments. We also demonstrate the effectiveness of our new approach by reporting on its improved performance on a set of 127 genomic regions known to regulate gene expression along the anterio-posterior axis in early Drosophila embryos.
Publication Title
PLoS ONE
Publication Date
10-1-2017
Volume
12
Issue
10
DOI
10.1371/journal.pone.0185570
Keywords
binding sites, machine learning, nucleotides, Support Vector Machine, transcription factors
Repository Citation
Elmas, Abdulkadir; Wang, Xiaodong; and Dresch, Jacqueline M., "The folded k-spectrum kernel: A machine learning approach to detecting transcription factor binding sites with gapped nucleotide dependencies" (2017). Biology. 117.
https://commons.clarku.edu/faculty_biology/117
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright Conditions
Elmas, A., Wang, X., & Dresch, J. M. (2017). The folded k-spectrum kernel: A machine learning approach to detecting transcription factor binding sites with gapped nucleotide dependencies. Plos one, 12(10), e0185570.
