Prediction of G Protein-Coupled Receptors with SVM-Prot Features and Random Forest
about
An Alignment-Free Algorithm in Comparing the Similarity of Protein Sequences Based on Pseudo-Markov Transition Probabilities among Amino Acids.Identifying the Types of Ion Channel-Targeted Conotoxins by Incorporating New Properties of Residues into Pseudo Amino Acid Composition.Identification of DEP domain-containing proteins by a machine learning method and experimental analysis of their expression in human HCC tissues.IonchanPred 2.0: A Tool to Predict Ion Channels and Their Types.Recent Advances in Conotoxin Classification by Using Machine Learning Methods.OmniGA: Optimized Omnivariate Decision Trees for Generalizable Classification Models.High-Performance Prediction of Human Estrogen Receptor Agonists Based on Chemical Structures.AmPEP: Sequence-based prediction of antimicrobial peptides using distribution patterns of amino acid properties and random forest.Predicting Diabetes Mellitus With Machine Learning Techniques
P2860
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P2860
Prediction of G Protein-Coupled Receptors with SVM-Prot Features and Random Forest
description
2016 nî lūn-bûn
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2016 թուականին հրատարակուած գիտական յօդուած
@hyw
2016 թվականին հրատարակված գիտական հոդված
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2016年の論文
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2016年学术文章
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2016年学术文章
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2016年学术文章
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2016年学术文章
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2016年学术文章
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2016年學術文章
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name
Prediction of G Protein-Coupled Receptors with SVM-Prot Features and Random Forest
@ast
Prediction of G Protein-Coupled Receptors with SVM-Prot Features and Random Forest
@en
type
label
Prediction of G Protein-Coupled Receptors with SVM-Prot Features and Random Forest
@ast
Prediction of G Protein-Coupled Receptors with SVM-Prot Features and Random Forest
@en
prefLabel
Prediction of G Protein-Coupled Receptors with SVM-Prot Features and Random Forest
@ast
Prediction of G Protein-Coupled Receptors with SVM-Prot Features and Random Forest
@en
P2860
P3181
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Prediction of G Protein-Coupled Receptors with SVM-Prot Features and Random Forest
@en
P2093
P2860
P304
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10.1155/2016/8309253
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P577
2016-07-27T00:00:00Z