A novel k-mer set memory (KSM) motif representation improves regulatory variant prediction

The representation and discovery of transcription factor (TF) sequence binding specificities is critical for understanding gene regulatory networks and interpreting the impact of disease-associated noncoding genetic variants. We present a novel TF binding motif representation, the k-mer set memory (...

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Bibliographic Details
Main Authors: Guo, Yuchun (Contributor), Tian, Kevin J. (Contributor), Zeng, Haoyang (Contributor), Guo, Xiaoyun (Contributor), Gifford, David K (Contributor)
Other Authors: Massachusetts Institute of Technology. Computational and Systems Biology Program (Contributor), Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory (Contributor), Massachusetts Institute of Technology. Department of Biological Engineering (Contributor), Massachusetts Institute of Technology. Department of Biology (Contributor), Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor), Massachusetts Institute of Technology. Department of Mathematics (Contributor), Massachusetts Institute of Technology. Research Laboratory of Electronics (Contributor)
Format: Article
Language:English
Published: Cold Spring Harbor Laboratory, 2018-12-17T13:34:30Z.
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