DeepQA: improving the estimation of single protein model quality with deep belief networks
Published 2016 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
DeepQA: improving the estimation of single protein model quality with deep belief networks
Authors
Keywords
Protein model quality assessment, Protein structure prediction, Machine learning, Deep belief network
Journal
BMC BIOINFORMATICS
Volume 17, Issue 1, Pages -
Publisher
Springer Nature
Online
2016-12-05
DOI
10.1186/s12859-016-1405-y
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- UniCon3D:de novoprotein structure prediction using united-residue conformational search via stepwise, probabilistic sampling
- (2016) Debswapna Bhattacharya et al. BIOINFORMATICS
- ProQ2: estimation of model accuracy implemented in Rosetta
- (2016) Karolis Uziela et al. BIOINFORMATICS
- ResQ: An Approach to Unified Estimation of B -Factor and Residue-Specific Error in Protein Structure Prediction
- (2016) Jianyi Yang et al. JOURNAL OF MOLECULAR BIOLOGY
- Integrated protein function prediction by mining function associations, sequences, and protein–protein and gene–gene interaction networks
- (2016) Renzhi Cao et al. METHODS
- Mastering the game of Go with deep neural networks and tree search
- (2016) David Silver et al. NATURE
- A Stochastic Point Cloud Sampling Method for Multi-Template Protein Comparative Modeling
- (2016) Jilong Li et al. Scientific Reports
- Benchmarking Deep Networks for Predicting Residue-Specific Quality of Individual Protein Models in CASP11
- (2016) Tong Liu et al. Scientific Reports
- Protein single-model quality assessment by feature-based probability density functions
- (2016) Renzhi Cao et al. Scientific Reports
- Large-scale model quality assessment for improving protein tertiary structure prediction
- (2015) Renzhi Cao et al. BIOINFORMATICS
- FALCON@home: a high-throughput protein structure prediction server based on remote homologue recognition
- (2015) Chao Wang et al. BIOINFORMATICS
- 3DRobot: automated generation of diverse and well-packed protein structure decoys
- (2015) Haiyou Deng et al. BIOINFORMATICS
- A large-scale conformation sampling and evaluation server for protein tertiary structure prediction and its assessment in CASP11
- (2015) Jilong Li et al. BMC BIOINFORMATICS
- Deep learning
- (2015) Yann LeCun et al. NATURE
- Massive integration of diverse protein quality assessment methods to improve template based modeling in CASP11
- (2015) Renzhi Cao et al. PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
- Methods of model accuracy estimation can help selecting the best models from decoy sets: Assessment of model accuracy estimations in CASP11
- (2015) Andriy Kryshtafovych et al. PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
- CONFOLD: Residue-residue contact-guidedab initioprotein folding
- (2015) Badri Adhikari et al. PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
- De novo protein conformational sampling using a probabilistic graphical model
- (2015) Debswapna Bhattacharya et al. Scientific Reports
- SMOQ: a tool for predicting the absolute residue-specific quality of a single protein model with support vector machines
- (2014) Renzhi Cao et al. BMC BIOINFORMATICS
- Random Forest-Based Protein Model Quality Assessment (RFMQA) Using Structural Features and Potential Energy Terms
- (2014) Balachandran Manavalan et al. PLoS One
- Designing and evaluating the MULTICOM protein local and global model quality prediction methods in the CASP10 experiment
- (2014) Renzhi Cao et al. BMC STRUCTURAL BIOLOGY
- Capturing native/native like structures with a physico-chemical metric (pcSM) in protein folding
- (2013) Avinash Mishra et al. BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS
- Predicting protein residue–residue contacts using deep networks and boosting
- (2012) Jesse Eickholt et al. BIOINFORMATICS
- Improved model quality assessment using ProQ2
- (2012) Arjun Ray et al. BMC BIOINFORMATICS
- GOAP: A Generalized Orientation-Dependent, All-Atom Statistical Potential for Protein Structure Prediction
- (2011) Hongyi Zhou et al. BIOPHYSICAL JOURNAL
- MUFOLD-WQA: A new selective consensus method for quality assessment in protein structure prediction
- (2011) Qingguo Wang et al. PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
- Raptorx: Exploiting structure information for protein alignment by statistical inference
- (2011) Jian Peng et al. PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
- Quality assessment of protein model-structures using evolutionary conservation
- (2010) Matan Kalman et al. BIOINFORMATICS
- Toward the estimation of the absolute quality of individual protein structure models
- (2010) Pascal Benkert et al. BIOINFORMATICS
- A Novel Side-Chain Orientation Dependent Potential Derived from Random-Walk Reference State for Protein Fold Selection and Structure Prediction
- (2010) Jian Zhang et al. PLoS One
- Rapid model quality assessment for protein structure predictions using the comparison of multiple models without structural alignments
- (2009) L. J. McGuffin et al. BIOINFORMATICS
- MUFOLD: A new solution for protein 3D structure prediction
- (2009) Jingfen Zhang et al. PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
- The ModFOLD server for the quality assessment of protein structural models
- (2008) L. J. McGuffin BIOINFORMATICS
- I-TASSER server for protein 3D structure prediction
- (2008) Yang Zhang BMC BIOINFORMATICS
- Fragment-HMM: A new approach to protein structure prediction
- (2008) Shuai Cheng Li et al. PROTEIN SCIENCE
- Specific interactions for ab initio folding of protein terminal regions with secondary structures
- (2008) Yuedong Yang et al. PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
- Evaluating the absolute quality of a single protein model using structural features and support vector machines
- (2008) Zheng Wang et al. PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
Find Funding. Review Successful Grants.
Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.
ExploreBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started