Prediction of protein subcellular localization with oversampling approach and Chou's general PseAAC

DOI10.1016/J.JTBI.2017.10.030zbMath1394.92047OpenAlexW2766117835WikidataQ47443685 ScholiaQ47443685MaRDI QIDQ1752399

Xin Duan, Sheng-Li Zhang

Publication date: 24 May 2018

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

Full work available at URL: https://doi.org/10.1016/j.jtbi.2017.10.030

zbMATH Keywords

oversamplingPCASVMPSSMPseAACjackknife tests

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Biochemistry, molecular biology (92C40)

Related Items (8)

Predicting protein submitochondrial locations by incorporating the pseudo-position specific scoring matrix into the general Chou's pseudo-amino acid composition ⋮ BlaPred: predicting and classifying \(\beta\)-lactamase using a 3-tier prediction system via Chou's general PseAAC ⋮ pLoc\_bal-mGneg: predict subcellular localization of Gram-negative bacterial proteins by quasi-balancing training dataset and general PseAAC ⋮ Identify Gram-negative bacterial secreted protein types by incorporating different modes of PSSM into Chou's general PseAAC via Kullback-Leibler divergence ⋮ Analysis and prediction of ion channel inhibitors by using feature selection and Chou's general pseudo amino acid composition ⋮ iPPI-PseAAC(CGR): identify protein-protein interactions by incorporating chaos game representation into PseAAC ⋮ pSSbond-PseAAC: prediction of disulfide bonding sites by integration of PseAAC and statistical moments ⋮ SPrenylC-PseAAC: a sequence-based model developed via Chou's 5-steps rule and general PseAAC for identifying S-prenylation sites in proteins

Uses Software

• LIBSVM
• Pse-in-One
• iDrug-Target
• iLoc-Virus
• iLoc-Gpos
• iLoc-Plant
• iDNA-Methyl
• iLoc-Animal
• iPPBS-Opt
• iSuc-PseOpt
• iLoc-Hum
• iLoc-Euk
• Plant-mPLoc
• PseKNC
• iPhos-PseEvo
• iRNA-AI
• pLoc-mAnimal
• pLoc-mVirus
• pLoc-mEuk
• iRNA-PseColl
• iATC-mHyb
• iRNA-2methyl
• iRNAm5C-PseDNC
• POSSUM
• iPreny-PseAAC
• iRSpot-EL
• Unb-DPC
• OOgenesis_Pred
• pLoc-mPlant
• Gpos-mPLoc
• Gneg-mPLoc
• Hum-mPLoc
• 2L-piRNA

Cites Work

• Unnamed Item
• A novel representation for apoptosis protein subcellular localization prediction using support vector machine
• Some remarks on protein attribute prediction and pseudo amino acid composition
• Gneg-mPLoc: a top-down strategy to enhance the quality of predicting subcellular localization of Gram-negative bacterial proteins
• Evaluation of regular and singular domain integrals with boundary-only discretization-theory and Fortran code
• \textbf{iLoc-Virus}: a multi-label learning classifier for identifying the subcellular localization of virus proteins with both single and multiple sites
• Prediction of the subcellular location of apoptosis proteins
• Prediction of apoptosis protein subcellular location using improved hybrid approach and pseudo-amino acid composition
• A protein structural classes prediction method based on PSI-BLAST profile

This page was built for publication: Prediction of protein subcellular localization with oversampling approach and Chou's general PseAAC