about
GPCRsclass: a web tool for the classification of amine type of G-protein-coupled receptorsClassifying RNA-binding proteins based on electrostatic propertiesSMpred: a support vector machine approach to identify structural motifs in protein structure without using evolutionary information.BLProt: prediction of bioluminescent proteins based on support vector machine and relieff feature selection.Identification of amino acid propensities that are strong determinants of linear B-cell epitope using neural networks.Prediction of multi-type membrane proteins in human by an integrated approach.Methodology development for predicting subcellular localization and other attributes of proteins.Predicting protein submitochondria locations by combining different descriptors into the general form of Chou's pseudo amino acid composition.A Prediction Model for Membrane Proteins Using Moments Based Features.iSS-PC: Identifying Splicing Sites via Physical-Chemical Properties Using Deep Sparse Auto-Encoder.A Treatise to Computational Approaches Towards Prediction of Membrane Protein and Its Subtypes.Protein Remote Homology Detection by Combining Chou's Pseudo Amino Acid Composition and Profile-Based Protein Representation.Statistical approach for lysosomal membrane proteins (LMPs) identification.PreDNA: accurate prediction of DNA-binding sites in proteins by integrating sequence and geometric structure information.Prediction of ketoacyl synthase family using reduced amino acid alphabets.Identification of novel biomass-degrading enzymes from genomic dark matter: Populating genomic sequence space with functional annotation.Discriminating lysosomal membrane protein types using dynamic neural network.Classification of nuclear receptors based on amino acid composition and dipeptide composition.iAFP-Ense: An Ensemble Classifier for Identifying Antifreeze Protein by Incorporating Grey Model and PSSM into PseAAC.A multi-label classifier for prediction membrane protein functional types in animal.Prediction of membrane proteins using split amino acid and ensemble classification.Predicting subcellular location of apoptosis proteins with pseudo amino acid composition: approach from amino acid substitution matrix and auto covariance transformation.
P2860
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P2860
description
2004 nî lūn-bûn
@nan
2004年の論文
@ja
2004年学术文章
@wuu
2004年学术文章
@zh
2004年学术文章
@zh-cn
2004年学术文章
@zh-hans
2004年学术文章
@zh-my
2004年学术文章
@zh-sg
2004年學術文章
@yue
2004年學術文章
@zh-hant
name
Application of SVM to predict membrane protein types.
@en
Application of SVM to predict membrane protein types.
@nl
type
label
Application of SVM to predict membrane protein types.
@en
Application of SVM to predict membrane protein types.
@nl
prefLabel
Application of SVM to predict membrane protein types.
@en
Application of SVM to predict membrane protein types.
@nl
P1476
Application of SVM to predict membrane protein types.
@en
P2093
Chih-Hung Jen
Pong-Wong Ricardo
P304
P356
10.1016/J.JTBI.2003.08.015
P407
P577
2004-02-01T00:00:00Z