FunSAV: predicting the functional effect of single amino acid variants using a two-stage random forest model
about
Analysis of genetic variation and potential applications in genome-scale metabolic modelingStatus quo of annotation of human disease variants.Accurate in silico identification of species-specific acetylation sites by integrating protein sequence-derived and functional features.SuSPect: enhanced prediction of single amino acid variant (SAV) phenotype using network featuresAn 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.Principal component analysis of binding energies for single-point mutants of hT2R16 bound to an agonist correlate with experimental mutant cell responsePredPPCrys: accurate prediction of sequence cloning, protein production, purification and crystallization propensity from protein sequences using multi-step heterogeneous feature fusion and selection.Case-only exome sequencing and complex disease susceptibility gene discovery: study design considerations.Accurate prediction of functional effects for variants by combining gradient tree boosting with optimal neighborhood properties.GlycoMine(struct): a new bioinformatics tool for highly accurate mapping of the human N-linked and O-linked glycoproteomes by incorporating structural features.Computational assessment of feature combinations for pathogenic variant prediction.Annotating Mutational Effects on Proteins and Protein Interactions: Designing Novel and Revisiting Existing Protocols.Critical evaluation of bioinformatics tools for the prediction of protein crystallization propensity.Prediction of aptamer-target interacting pairs with pseudo-amino acid composition.Computational characterization of parallel dimeric and trimeric coiled-coils using effective amino acid indices.Cascleave 2.0, a new approach for predicting caspase and granzyme cleavage targets.DHSpred: support-vector-machine-based human DNase I hypersensitive sites prediction using the optimal features selected by random forest.Discriminating between deleterious and neutral non-frameshifting indels based on protein interaction networks and hybrid properties.H-DROP: an SVM based helical domain linker predictor trained with features optimized by combining random forest and stepwise selection.PVP-SVM: Sequence-Based Prediction of Phage Virion Proteins Using a Support Vector Machine.
P2860
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P2860
FunSAV: predicting the functional effect of single amino acid variants using a two-stage random forest model
description
2012 nî lūn-bûn
@nan
2012 թուականի Օգոստոսին հրատարակուած գիտական յօդուած
@hyw
2012 թվականի օգոստոսին հրատարակված գիտական հոդված
@hy
2012年の論文
@ja
2012年論文
@yue
2012年論文
@zh-hant
2012年論文
@zh-hk
2012年論文
@zh-mo
2012年論文
@zh-tw
2012年论文
@wuu
name
FunSAV: predicting the functio ...... two-stage random forest model
@ast
FunSAV: predicting the functio ...... two-stage random forest model
@en
FunSAV: predicting the functio ...... two-stage random forest model
@nl
type
label
FunSAV: predicting the functio ...... two-stage random forest model
@ast
FunSAV: predicting the functio ...... two-stage random forest model
@en
FunSAV: predicting the functio ...... two-stage random forest model
@nl
prefLabel
FunSAV: predicting the functio ...... two-stage random forest model
@ast
FunSAV: predicting the functio ...... two-stage random forest model
@en
FunSAV: predicting the functio ...... two-stage random forest model
@nl
P2093
P2860
P1433
P1476
FunSAV: predicting the functio ...... two-stage random forest model
@en
P2093
Haisong Xu
Mingjun Wang
Tatsuya Akutsu
Xing-Ming Zhao
P2860
P304
P356
10.1371/JOURNAL.PONE.0043847
P407
P577
2012-08-24T00:00:00Z