A deep learning framework for improving long‐range residue‐residue contact prediction using a hierarchical strategy
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[1] Gregory B. Gloor,et al. Mutual information without the influence of phylogeny or entropy dramatically improves residue contact prediction , 2008, Bioinform..
[2] De-Shuang Huang,et al. Prediction of inter-residue contacts map based on genetic algorithm optimized radial basis function neural network and binary input encoding scheme , 2004, J. Comput. Aided Mol. Des..
[3] David T. Jones,et al. MetaPSICOV: combining coevolution methods for accurate prediction of contacts and long range hydrogen bonding in proteins , 2014, Bioinform..
[4] Jing Yang,et al. R2C: improving ab initio residue contact map prediction using dynamic fusion strategy and Gaussian noise filter , 2016, Bioinform..
[5] Burkhard Rost,et al. PROFcon: novel prediction of long-range contacts , 2005, Bioinform..
[6] David T. Jones,et al. Accurate contact predictions using covariation techniques and machine learning , 2015, Proteins.
[7] Wen-Lian Hsu,et al. Predicting RNA-binding sites of proteins using support vector machines and evolutionary information , 2008, BMC Bioinformatics.
[8] A. Tramontano,et al. New encouraging developments in contact prediction: Assessment of the CASP11 results , 2016, Proteins.
[9] Marcin J. Skwark,et al. Improved Contact Predictions Using the Recognition of Protein Like Contact Patterns , 2014, PLoS Comput. Biol..
[10] Dongsup Kim,et al. A new method for revealing correlated mutations under the structural and functional constraints in proteins , 2009, Bioinform..
[11] O. Brock,et al. Combining Physicochemical and Evolutionary Information for Protein Contact Prediction , 2014, PloS one.
[12] D. Baker,et al. Assessing the utility of coevolution-based residue–residue contact predictions in a sequence- and structure-rich era , 2013, Proceedings of the National Academy of Sciences.
[13] Geoffrey E. Hinton,et al. Reducing the Dimensionality of Data with Neural Networks , 2006, Science.
[14] Zhiyong Wang,et al. Predicting protein contact map using evolutionary and physical constraints by integer programming , 2013, Bioinform..
[15] Dapeng Xiong,et al. RBRIdent: An algorithm for improved identification of RNA‐binding residues in proteins from primary sequences , 2015, Proteins.
[16] J. Skolnick,et al. TOUCHSTONE II: a new approach to ab initio protein structure prediction. , 2003, Biophysical journal.
[17] Magnus Ekeberg,et al. Fast pseudolikelihood maximization for direct-coupling analysis of protein structure from many homologous amino-acid sequences , 2014, J. Comput. Phys..
[18] Massimiliano Pontil,et al. PSICOV: precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence alignments , 2012, Bioinform..
[19] Markus Gruber,et al. CCMpred—fast and precise prediction of protein residue–residue contacts from correlated mutations , 2014, Bioinform..
[20] Marcin J. Skwark,et al. PconsFold: improved contact predictions improve protein models , 2014, Bioinform..
[21] J. Skolnick,et al. Development and large scale benchmark testing of the PROSPECTOR_3 threading algorithm , 2004, Proteins.
[22] W. Kabsch,et al. Dictionary of protein secondary structure: Pattern recognition of hydrogen‐bonded and geometrical features , 1983, Biopolymers.
[23] Haim Ashkenazy,et al. Peptides modulating conformational changes in secreted chaperones: From in silico design to preclinical proof of concept , 2009, Proceedings of the National Academy of Sciences.
[24] Jianlin Cheng,et al. Predicting protein residue-residue contacts using deep networks and boosting , 2012, Bioinform..
[25] Yee Whye Teh,et al. A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.
[26] George Karypis,et al. Prediction of contact maps using support vector machines , 2003, Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings..
[27] Jianlin Cheng,et al. CONFOLD: Residue‐residue contact‐guided ab initio protein folding , 2015, Proteins.
[28] Christopher Bystroff,et al. Predicting interresidue contacts using templates and pathways , 2003, Proteins.
[29] Anna Tramontano,et al. Evaluation of residue–residue contact prediction in CASP10 , 2014, Proteins.
[30] Torgeir R. Hvidsten,et al. Using multi-data hidden Markov models trained on local neighborhoods of protein structure to predict residue-residue contacts , 2009, Bioinform..
[31] Jianwen Fang,et al. Predicting residue-residue contacts using random forest models , 2011, Bioinform..
[32] A. Tramontano,et al. Evaluation of residue–residue contact predictions in CASP9 , 2011, Proteins.
[33] Jian Huang,et al. A Selective Review of Group Selection in High-Dimensional Models. , 2012, Statistical science : a review journal of the Institute of Mathematical Statistics.
[34] Jianlin Cheng,et al. NNcon: improved protein contact map prediction using 2D-recursive neural networks , 2009, Nucleic Acids Res..
[35] David S. Eisenberg,et al. Using inferred residue contacts to distinguish between correct and incorrect protein models , 2008, Bioinform..
[36] Daniel Y. Little,et al. Identification of Coevolving Residues and Coevolution Potentials Emphasizing Structure, Bond Formation and Catalytic Coordination in Protein Evolution , 2009, PloS one.
[37] Zhiyong Wang,et al. Protein contact prediction by integrating joint evolutionary coupling analysis and supervised learning , 2013, Bioinform..
[38] Steven E. Brenner,et al. SCOPe: Structural Classification of Proteins—extended, integrating SCOP and ASTRAL data and classification of new structures , 2013, Nucleic Acids Res..
[39] Piero Fariselli,et al. Reconstruction of 3D Structures From Protein Contact Maps , 2008, IEEE ACM Trans. Comput. Biol. Bioinform..
[40] Yang Zhang,et al. A comprehensive assessment of sequence-based and template-based methods for protein contact prediction , 2008, Bioinform..
[41] Bin Xue,et al. Predicting residue–residue contact maps by a two‐layer, integrated neural‐network method , 2009, Proteins.
[42] M. Tress,et al. Predicted residue–residue contacts can help the scoring of 3D models , 2010, Proteins.
[43] Jian Huang,et al. Penalized methods for bi-level variable selection. , 2009, Statistics and its interface.
[44] Shek-Chung Yau,et al. Protein space: a natural method for realizing the nature of protein universe. , 2013, Journal of theoretical biology.
[45] Thomas A. Hopf,et al. Protein 3D Structure Computed from Evolutionary Sequence Variation , 2011, PloS one.
[46] David E. Kim,et al. Physically realistic homology models built with ROSETTA can be more accurate than their templates. , 2006, Proceedings of the National Academy of Sciences of the United States of America.
[47] C. Sander,et al. Direct-coupling analysis of residue coevolution captures native contacts across many protein families , 2011, Proceedings of the National Academy of Sciences.
[48] Pierre Baldi,et al. Deep architectures for protein contact map prediction , 2012, Bioinform..
[49] Taghi M. Khoshgoftaar,et al. Deep learning applications and challenges in big data analytics , 2015, Journal of Big Data.
[50] E. Aurell,et al. Improved contact prediction in proteins: using pseudolikelihoods to infer Potts models. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.
[51] T. Hwa,et al. Identification of direct residue contacts in protein–protein interaction by message passing , 2009, Proceedings of the National Academy of Sciences.
[52] Pierre Baldi,et al. SSpro/ACCpro 5: almost perfect prediction of protein secondary structure and relative solvent accessibility using profiles, machine learning and structural similarity , 2014, Bioinform..
[53] Pierre Baldi,et al. Improved residue contact prediction using support vector machines and a large feature set , 2007, BMC Bioinformatics.