Identification of voltage-gated potassium channel subfamilies from sequence information using support vector machine.
about
Bioinformatics approaches for functional annotation of membrane proteinsPSIONplus: Accurate Sequence-Based Predictor of Ion Channels and Their TypesIdentifying the subfamilies of voltage-gated potassium channels using feature selection technique.Predicting cancerlectins by the optimal g-gap dipeptides.Naïve Bayes classifier with feature selection to identify phage virion proteinsiACP: a sequence-based tool for identifying anticancer peptides.Identification of apolipoprotein using feature selection technique.Identification of antioxidants from sequence information using naïve Bayes.Deorphanizing the human transmembrane genome: A landscape of uncharacterized membrane proteinsA Hybrid Classification System for Heart Disease Diagnosis Based on the RFRS Method.IonchanPred 2.0: A Tool to Predict Ion Channels and Their Types.Recombination Hotspot/Coldspot Identification Combining Three Different Pseudocomponents via an Ensemble Learning Approach.Prediction of drugs target groups based on ChEBI ontology.Identification of Bacterial Cell Wall Lyases via Pseudo Amino Acid Composition.PhD7Faster: predicting clones propagating faster from the Ph.D.-7 phage display peptide library.Accurate Identification of Cancerlectins through Hybrid Machine Learning Technology.Special Protein Molecules Computational Identification.Protein binding site prediction by combining hidden Markov support vector machine and profile-based propensities.Briefing in application of machine learning methods in ion channel prediction.Prediction of DNase I hypersensitive sites by using pseudo nucleotide compositions.
P2860
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P2860
Identification of voltage-gated potassium channel subfamilies from sequence information using support vector machine.
description
2012 nî lūn-bûn
@nan
2012年の論文
@ja
2012年学术文章
@wuu
2012年学术文章
@zh
2012年学术文章
@zh-cn
2012年学术文章
@zh-hans
2012年学术文章
@zh-my
2012年学术文章
@zh-sg
2012年學術文章
@yue
2012年學術文章
@zh-hant
name
Identification of voltage-gate ...... using support vector machine.
@en
Identification of voltage-gate ...... using support vector machine.
@nl
type
label
Identification of voltage-gate ...... using support vector machine.
@en
Identification of voltage-gate ...... using support vector machine.
@nl
prefLabel
Identification of voltage-gate ...... using support vector machine.
@en
Identification of voltage-gate ...... using support vector machine.
@nl
P1476
Identification of voltage-gate ...... using support vector machine.
@en
P2093
P304
P356
10.1016/J.COMPBIOMED.2012.01.003
P50
P577
2012-01-31T00:00:00Z