Combining Physicochemical and Evolutionary Information for Protein Contact Prediction
出版年份 2014 全文链接
标题
Combining Physicochemical and Evolutionary Information for Protein Contact Prediction
作者
关键词
-
出版物
PLoS One
Volume 9, Issue 10, Pages e108438
出版商
Public Library of Science (PLoS)
发表日期
2014-10-23
DOI
10.1371/journal.pone.0108438
参考文献
相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。- De Novo Structure Prediction of Globular Proteins Aided by Sequence Variation-Derived Contacts
- (2014) Tomasz Kosciolek et al. PLoS One
- Predicting protein contact map using evolutionary and physical constraints by integer programming
- (2013) Z. Wang et al. BIOINFORMATICS
- Assessing the utility of coevolution-based residue-residue contact predictions in a sequence- and structure-rich era
- (2013) H. Kamisetty et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- Evaluation of residue-residue contact prediction in CASP10
- (2013) Bohdan Monastyrskyy et al. PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
- Deep architectures for protein contact map prediction
- (2012) Pietro Di Lena et al. BIOINFORMATICS
- Predicting protein residue–residue contacts using deep networks and boosting
- (2012) Jesse Eickholt et al. BIOINFORMATICS
- Efficient sampling of protein conformational space using fast loop building and batch minimization on highly parallel computers
- (2012) Michael D. Tyka et al. JOURNAL OF COMPUTATIONAL CHEMISTRY
- Protein structure prediction from sequence variation
- (2012) Debora S Marks et al. NATURE BIOTECHNOLOGY
- A Position-Specific Distance-Dependent Statistical Potential for Protein Structure and Functional Study
- (2012) Feng Zhao et al. STRUCTURE
- Effective graph classification based on topological and label attributes
- (2012) Geng Li et al. Statistical Analysis and Data Mining
- Predicting residue–residue contacts using random forest models
- (2011) Yunqi Li et al. BIOINFORMATICS
- PSICOV: precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence alignments
- (2011) David T. Jones et al. BIOINFORMATICS
- HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment
- (2011) Michael Remmert et al. NATURE METHODS
- Protein 3D Structure Computed from Evolutionary Sequence Variation
- (2011) Debora S. Marks et al. PLoS One
- Evaluation of residue-residue contact predictions in CASP9
- (2011) Bohdan Monastyrskyy et al. PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
- Improving Protein Structure Prediction Using Multiple Sequence-Based Contact Predictions
- (2011) Sitao Wu et al. STRUCTURE
- Using multi-data hidden Markov models trained on local neighborhoods of protein structure to predict residue–residue contacts
- (2009) Patrik Björkholm et al. BIOINFORMATICS
- Learning from Imbalanced Data
- (2009) Haibo He et al. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
- BCL::Contact–Low Confidence Fold Recognition Hits Boost Protein Contact Prediction and De Novo Structure Determination
- (2009) Mert Karakaş et al. JOURNAL OF COMPUTATIONAL BIOLOGY
- NNcon: improved protein contact map prediction using 2D-recursive neural networks
- (2009) A. N. Tegge et al. NUCLEIC ACIDS RESEARCH
- SAM-T08, HMM-based protein structure prediction
- (2009) K. Karplus NUCLEIC ACIDS RESEARCH
- Defining an Essence of Structure Determining Residue Contacts in Proteins
- (2009) R. Sathyapriya et al. PLoS Computational Biology
- FT-COMAR: fault tolerant three-dimensional structure reconstruction from protein contact maps
- (2008) Marco Vassura et al. BIOINFORMATICS
- Prediction of protein functional residues from sequence by probability density estimation
- (2008) J. D. Fischer et al. BIOINFORMATICS
- A comprehensive assessment of sequence-based and template-based methods for protein contact prediction
- (2008) Sitao Wu et al. BIOINFORMATICS
- Reconstruction of 3D Structures From Protein Contact Maps
- (2008) M. Vassura et al. IEEE-ACM Transactions on Computational Biology and Bioinformatics
Discover Peeref hubs
Discuss science. Find collaborators. Network.
Join a conversationBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started