期刊
BIOINFORMATICS
卷 28, 期 23, 页码 3066-3072出版社
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/bts598
关键词
-
类别
资金
- National Library of Medicine Biomedical and Health Informatics Training fellowship
- NIH NIGMS [R01GM093123]
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据