Random forest-based protein model quality assessment (RFMQA) using structural features and potential energy terms
about
Structural Mechanism behind Distinct Efficiency of Oct4/Sox2 Proteins in Differentially Spaced DNA ComplexesDeepQA: improving the estimation of single protein model quality with deep belief networksSigma-RF: prediction of the variability of spatial restraints in template-based modeling by random forest.Sorting protein decoys by machine-learning-to-rankMQAPRank: improved global protein model quality assessment by learning-to-rank.Automatic ICD-10 multi-class classification of cause of death from plaintext autopsy reports through expert-driven feature selectionSVMQA: support-vector-machine-based protein single-model quality assessment.ProQ2: estimation of model accuracy implemented in Rosetta.In silico prediction of multiple-category classification model for cytochrome P450 inhibitors and non-inhibitors using machine-learning method.MLACP: machine-learning-based prediction of anticancer peptides.Methods for estimation of model accuracy in CASP12.DHSpred: support-vector-machine-based human DNase I hypersensitive sites prediction using the optimal features selected by random forest.PClass: Protein Quaternary Structure Classification by Using Bootstrapping Strategy as Model Selection.AIPpred: Sequence-Based Prediction of Anti-inflammatory Peptides Using Random Forest.PVP-SVM: Sequence-Based Prediction of Phage Virion Proteins Using a Support Vector Machine.Prediction of Antitubercular Peptides From Sequence Information Using Ensemble Classifier and Hybrid FeaturesiGHBP: Computational identification of growth hormone binding proteins from sequences using extremely randomised tree
P2860
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P2860
Random forest-based protein model quality assessment (RFMQA) using structural features and potential energy terms
description
2014 nî lūn-bûn
@nan
2014 թուականի Սեպտեմբերին հրատարակուած գիտական յօդուած
@hyw
2014 թվականի սեպտեմբերին հրատարակված գիտական հոդված
@hy
2014年の論文
@ja
2014年論文
@yue
2014年論文
@zh-hant
2014年論文
@zh-hk
2014年論文
@zh-mo
2014年論文
@zh-tw
2014年论文
@wuu
name
Random forest-based protein mo ...... res and potential energy terms
@ast
Random forest-based protein mo ...... res and potential energy terms
@en
type
label
Random forest-based protein mo ...... res and potential energy terms
@ast
Random forest-based protein mo ...... res and potential energy terms
@en
prefLabel
Random forest-based protein mo ...... res and potential energy terms
@ast
Random forest-based protein mo ...... res and potential energy terms
@en
P2860
P1433
P1476
Random forest-based protein mo ...... res and potential energy terms
@en
P2093
Balachandran Manavalan
Jooyoung Lee
P2860
P304
P356
10.1371/JOURNAL.PONE.0106542
P407
P50
P577
2014-09-15T00:00:00Z