Predicting residue-residue contacts and helix-helix interactions in transmembrane proteins using an integrative feature-based random forest approach
about
Sequence-structure relationship study in all-α transmembrane proteins using an unsupervised learning approach.COMSAT: Residue contact prediction of transmembrane proteins based on support vector machines and mixed integer linear programming.Residue contacts predicted by evolutionary covariance extend the application of ab initio molecular replacement to larger and more challenging protein folds.Computational Approaches for Revealing the Structure of Membrane Transporters: Case Study on Bilitranslocase.Accurate in silico identification of species-specific acetylation sites by integrating protein sequence-derived and functional features.FunSAV: predicting the functional effect of single amino acid variants using a two-stage random forest modelAn integrative computational framework based on a two-step random forest algorithm improves prediction of zinc-binding sites in proteinsPROSPER: an integrated feature-based tool for predicting protease substrate cleavage sites.Protein inter-domain linker prediction using Random Forest and amino acid physiochemical propertiesPredPPCrys: accurate prediction of sequence cloning, protein production, purification and crystallization propensity from protein sequences using multi-step heterogeneous feature fusion and selection.DephosSite: a machine learning approach for discovering phosphotase-specific dephosphorylation sites.High-accuracy prediction of transmembrane inter-helix contacts and application to GPCR 3D structure modeling.Predicting the helix-helix interactions from correlated residue mutations.SOHPRED: a new bioinformatics tool for the characterization and prediction of human S-sulfenylation sites.AAFreqCoil: a new classifier to distinguish parallel dimeric and trimeric coiled coils.RRCRank: a fusion method using rank strategy for residue-residue contact predictionWRF-TMH: predicting transmembrane helix by fusing composition index and physicochemical properties of amino acids.MemBrain-contact 2.0: A new two-stage machine learning model for the prediction enhancement of transmembrane protein residue contacts in the full chain.Computational characterization of parallel dimeric and trimeric coiled-coils using effective amino acid indices.Forecasting residue-residue contact prediction accuracy.RFAthM6A: a new tool for predicting m6A sites in Arabidopsis thaliana.Folding Membrane Proteins by Deep Transfer Learning.
P2860
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P2860
Predicting residue-residue contacts and helix-helix interactions in transmembrane proteins using an integrative feature-based random forest approach
description
2011 nî lūn-bûn
@nan
2011 թուականի Հոկտեմբերին հրատարակուած գիտական յօդուած
@hyw
2011 թվականի հոտեմբերին հրատարակված գիտական հոդված
@hy
2011年の論文
@ja
2011年論文
@yue
2011年論文
@zh-hant
2011年論文
@zh-hk
2011年論文
@zh-mo
2011年論文
@zh-tw
2011年论文
@wuu
name
Predicting residue-residue con ...... e-based random forest approach
@ast
Predicting residue-residue con ...... e-based random forest approach
@en
Predicting residue-residue con ...... e-based random forest approach
@nl
type
label
Predicting residue-residue con ...... e-based random forest approach
@ast
Predicting residue-residue con ...... e-based random forest approach
@en
Predicting residue-residue con ...... e-based random forest approach
@nl
prefLabel
Predicting residue-residue con ...... e-based random forest approach
@ast
Predicting residue-residue con ...... e-based random forest approach
@en
Predicting residue-residue con ...... e-based random forest approach
@nl
P2093
P2860
P1433
P1476
Predicting residue-residue con ...... e-based random forest approach
@en
P2093
Chuan Wang
Ren-Xiang Yan
Xiao-Feng Wang
P2860
P304
P356
10.1371/JOURNAL.PONE.0026767
P407
P577
2011-10-28T00:00:00Z