Comprehensive comparative analysis and identification of RNA-binding protein domains: multi-class classification and feature selection

DOI10.1016/j.jtbi.2012.07.013zbMath1337.92062OpenAlexW2142046193WikidataQ38495608 ScholiaQ38495608MaRDI QIDQ293789

Degui Zhi, Vinodh Srinivasasainagendra, Samad Jahandideh

Publication date: 9 June 2016

Published in: Journal of Theoretical Biology (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc3867591

zbMATH Keywords

predictionrandom forestmulti-class \(\ell_1/\ell_q\)-regularized logistic regressionRNA-binding domaintuned multi-class SVM

Mathematics Subject Classification ID

Biochemistry, molecular biology (92C40) Protein sequences, DNA sequences (92D20)

Uses Software

• Cell-PLoc
• randomForest
• iDNA-Prot
• iLoc-Virus
• iLoc-Plant
• iLoc-Hum
• iLoc-Euk
• AFP-Pred
• RSARF
• SecretP
• OligoPred
• Hum-PLoc
• iNR-PhysChem
• ASAView

Cites Work

• Sparse regression using mixed norms
• Use of fuzzy clustering technique and matrices to classify amino acids and its impact to Chou's pseudo amino acid composition
• Predicting DNA- and RNA-binding proteins from sequences with kernel methods
• Using the augmented Chou's pseudo amino acid composition for predicting protein submitochondria locations based on auto covariance approach
• Some remarks on protein attribute prediction and pseudo amino acid composition
• Using the concept of Chou's pseudo amino acid composition for risk type prediction of human papillomaviruses
• SecretP: identifying bacterial secreted proteins by fusing new features into Chou's pseudo-amino acid composition
• Prediction of GABA\(_{\mathrm A}\) receptor proteins using the concept of Chou's pseudo-amino acid composition and support vector machine
• \textbf{iLoc-Virus}: a multi-label learning classifier for identifying the subcellular localization of virus proteins with both single and multiple sites
• The modified Mahalanobis discriminant for predicting outer membrane proteins by using Chou's pseudo amino acid composition
• Predicting rRNA-, RNA-, and DNA-binding proteins from primary structure with support vector machines
• Using Chou's amphiphilic pseudo-amino acid composition and support vector machine for prediction of enzyme subfamily classes
• Multi-kernel transfer learning based on Chou's PseAAC formulation for protein submitochondria localization
• Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data
• Model Selection and Estimation in Regression with Grouped Variables
• Random forests
• A unified framework for high-dimensional analysis of \(M\)-estimators with decomposable regularizers
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