Sequence-based identification of recombination spots using pseudo nucleic acid representation and recursive feature extraction by linear kernel SVM

PhosphoSVM: prediction of phosphorylation sites by integrating various protein sequence attributes with a support vector machine

(2014) Yongchao Dou et al.   AMINO ACIDS

Identification of biomarkers that distinguish chemical contaminants based on gene expression profiles

(2014) Xiaomou Wei et al.   BMC GENOMICS

Prediction and classification of ncRNAs using structural information

(2014) Bharat Panwar et al.   BMC GENOMICS

Prediction of Protein S-Nitrosylation Sites Based on Adapted Normal Distribution Bi-Profile Bayes and Chou’s Pseudo Amino Acid Composition

(2014) Cangzhi Jia et al.   INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES

iHyd-PseAAC: Predicting Hydroxyproline and Hydroxylysine in Proteins by Incorporating Dipeptide Position-Specific Propensity into Pseudo Amino Acid Composition

(2014) Yan Xu et al.   INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES

iRSpot-TNCPseAAC: Identify Recombination Spots with Trinucleotide Composition and Pseudo Amino Acid Components

(2014) Wang-Ren Qiu et al.   INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES

Improving enzyme regulatory protein classification by means of SVM-RFE feature selection

(2014) Carlos Fernandez-Lozano et al.   Molecular BioSystems

Using Multi-Instance Hierarchical Clustering Learning System to Predict Yeast Gene Function

(2014) Bo Liao et al.   PLoS One

PSSP-RFE: Accurate Prediction of Protein Structural Class by Recursive Feature Extraction from PSI-BLAST Profile, Physical-Chemical Property and Functional Annotations

(2014) Liqi Li et al.   PLoS One

Supervised Learning Classification Models for Prediction of Plant Virus Encoded RNA Silencing Suppressors

(2014) Zeenia Jagga et al.   PLoS One

In silico site of metabolism prediction for human UGT-catalyzed reactions

(2013) Jianlong Peng et al.   BIOINFORMATICS

Combining optimization and machine learning techniques for genome-wide prediction of human cell cycle-regulated genes

(2013) Marianna De Santis et al.   BIOINFORMATICS

A support vector machine for identification of single-nucleotide polymorphisms from next-generation sequencing data

(2013) B. D. O'Fallon et al.   BIOINFORMATICS

NeuroPID: a predictor for identifying neuropeptide precursors from metazoan proteomes

(2013) Dan Ofer et al.   BIOINFORMATICS

Accurate prediction of bacterial type IV secreted effectors using amino acid composition and PSSM profiles

(2013) Lingyun Zou et al.   BIOINFORMATICS

Using radial basis function on the general form of Chou's pseudo amino acid composition and PSSM to predict subcellular locations of proteins with both single and multiple sites

(2013) Chao Huang et al.   BIOSYSTEMS

Sparse logistic regression with a L1/2 penalty for gene selection in cancer classification

(2013) Yong Liang et al.   BMC BIOINFORMATICS

iRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition

(2013) Wei Chen et al.   NUCLEIC ACIDS RESEARCH

iSNO-PseAAC: Predict Cysteine S-Nitrosylation Sites in Proteins by Incorporating Position Specific Amino Acid Propensity into Pseudo Amino Acid Composition

(2013) Yan Xu et al.   PLoS One

Using Amino Acid Factor Scores to Predict Avian-to-Human Transmission of Avian Influenza Viruses: A Machine Learning Study

(2013) Jia Wang et al.   PROTEIN AND PEPTIDE LETTERS

ECOH: An Enzyme Commission number predictor using mutual information and a support vector machine

(2012) Yoshihiko Matsuta et al.   BIOINFORMATICS

Identifying protein quaternary structural attributes by incorporating physicochemical properties into the general form of Chou's PseAAC via discrete wavelet transform

(2012) Xing-Yu Sun et al.   Molecular BioSystems

An Ensemble Classifier for Eukaryotic Protein Subcellular Location Prediction Using Gene Ontology Categories and Amino Acid Hydrophobicity

(2012) Liqi Li et al.   PLoS One

Prediction of Protein Subcellular Multi-Localization Based on the General form of Chou’s Pseudo Amino Acid Composition

(2012) Li-Qi Li et al.   PROTEIN AND PEPTIDE LETTERS