Direct AUC optimization of regulatory motifs

Motivation: The discovery of transcription factor binding site (TFBS) motifs is essential for untangling the complex mechanism of genetic variation under different developmental and environmental conditions. Among the huge amount of computational approaches for de novo identification of TFBS motifs,...

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Bibliographic Details
Published in:Bioinformatics
Main Authors: Zhu, Lin, Zhang, Hong-Bo, Huang, De-Shuang
Format: Text
Language:English
Published: Oxford University Press 2017
Subjects:
Ismb/Eccb 2017: The 25th Annual Conference Intelligent Systems for Molecular Biology Held Jointly with the 16th Annual European Conference on Computational Biology
Prague
Czech Republic
July 21–25
2017
DML
Online Access:http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5870558/
http://www.ncbi.nlm.nih.gov/pubmed/28881989
https://doi.org/10.1093/bioinformatics/btx255
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Summary:Motivation: The discovery of transcription factor binding site (TFBS) motifs is essential for untangling the complex mechanism of genetic variation under different developmental and environmental conditions. Among the huge amount of computational approaches for de novo identification of TFBS motifs, discriminative motif learning (DML) methods have been proven to be promising for harnessing the discovery power of accumulated huge amount of high-throughput binding data. However, they have to sacrifice accuracy for speed and could fail to fully utilize the information of the input sequences.

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