Journal
BIOINFORMATICS
Volume 28, Issue 23, Pages 3066-3072Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/bts598
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Funding
- National Library of Medicine Biomedical and Health Informatics Training fellowship
- NIH NIGMS [R01GM093123]
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Motivation: Protein residue-residue contacts continue to play a larger and larger role in protein tertiary structure modeling and evaluation. Yet, while the importance of contact information increases, the performance of sequence-based contact predictors has improved slowly. New approaches and methods are needed to spur further development and progress in the field. Results: Here we present DNCON, a new sequence-based residue-residue contact predictor using deep networks and boosting techniques. Making use of graphical processing units and CUDA parallel computing technology, we are able to train large boosted ensembles of residue-residue contact predictors achieving state-of-the-art performance.
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