iPPI-PseAAC(CGR): identify protein-protein interactions by incorporating chaos game representation into PseAAC

DOI10.1016/j.jtbi.2018.10.021zbMath1406.92189OpenAlexW2897596315WikidataQ57480510 ScholiaQ57480510MaRDI QIDQ1716822

Xuan Xiao, Kuo-Chen Chou, Xiao-Yan Li, Wang-Ren Qiu, Jian-Hua Jia

Publication date: 5 February 2019

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

Full work available at URL: https://doi.org/10.1016/j.jtbi.2018.10.021

zbMATH Keywords

random forestscross validation\textit{Saccharomyces cerevisiae}pseudo amino acid compositionchaos game representation\textit{Helicobacter pylori}web-server

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Learning and adaptive systems in artificial intelligence (68T05) Biochemistry, molecular biology (92C40) Protein sequences, DNA sequences (92D20) Computational methods for problems pertaining to biology (92-08)

Related Items (7)

The preliminary efficacy evaluation of the CTLA-4-ig treatment against lupus nephritis through \textit{in-silico} analyses ⋮ pSSbond-PseAAC: prediction of disulfide bonding sites by integration of PseAAC and statistical moments ⋮ Analysis and prediction of animal toxins by various Chou's pseudo components and reduced amino acid compositions ⋮ Predicting protein-protein interactions by fusing various Chou's pseudo components and using wavelet denoising approach ⋮ iRNA-PseKNC(2methyl): identify RNA 2'-O-methylation sites by convolution neural network and Chou's pseudo components ⋮ Identifying N\(^6\)-methyladenosine sites using extreme gradient boosting system optimized by particle swarm optimizer ⋮ 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

• LogitBoost
• PredLactamase
• iNuc-PseKNC
• pSuc-Lys
• iEnhancer-2L
• 2D-MH
• propy
• Pse-in-One
• iDNA-Prot
• iPro54-PseKNC
• iRSpot-TNCPseAAC
• iPPI-Esml
• iDrug-Target
• Prnam-PC
• iDNA-Methyl
• iMiRNA-PseDPC
• iPPBS-Opt
• iSuc-PseOpt
• PseKNC
• iRSpot-PseDNC
• iSS-PseDNC
• iRNA-Methyl
• iCTX-Type
• AFP-Pred
• RSARF
• iMethyl-PseAAC
• iSNO-PseAAC
• iSNO-AAPair
• iTIS-PseTNC
• iNitro-Tyr
• iNuc-PhysChem
• iPTM-mLys
• pSumo-CD
• iACP
• iCar-PseCp
• iHyd-PseCp
• iPhos-PseEvo
• iPhos-PseEn
• iOri-Human
• iRNA-AI
• pLoc-mAnimal
• pLoc-mVirus
• pLoc-mEuk
• iRNA-PseColl
• iATC-mHyb
• iRNA-2methyl
• iRNAm5C-PseDNC
• POSSUM
• iPreny-PseAAC
• DPP-PseAAC
• iRSpot-EL
• iPromoter-2L
• PREvaIL
• OOgenesis_Pred
• pLoc-mGneg
• pLoc-mPlant
• iHSP-PseRAAAC
• iKcr-PseEns
• iROS-gPseKNC
• iProt-Sub
• iRNA-PseU
• iRNA-3typeA
• pLoc-mHum
• pLoc_bal-mHum
• iDNA6mA-PseKNC
• iPPI-PseAAC
• Quokka
• 2L-piRNA
• iEnhancer-EL
• iRO-3wPseKNC
• pLoc_bal-mGpos
• iHyd-PseAAC
• iRSpot-Pse6NC
• BlaPred
• iFeature
• pLoc_bal-mGneg
• iLoc-lncRNA
• PROSPERous
• SMOTE
• FoldRate

Cites Work

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• Predicting plant protein subcellular multi-localization by Chou's PseAAC formulation based multi-label homolog knowledge transfer learning
• pSuc-Lys: predict lysine succinylation sites in proteins with PseAAC and ensemble random forest approach
• Prediction of protein structure classes by incorporating different protein descriptors into general Chou's pseudo amino acid composition
• Use of fuzzy clustering technique and matrices to classify amino acids and its impact to Chou's pseudo amino acid composition
• A novel feature representation method based on Chou's pseudo amino acid composition for protein structural class prediction
• IMem-2LSAAC: a two-level model for discrimination of membrane proteins and their types by extending the notion of SAAC into Chou's pseudo amino acid composition
• Some remarks on protein attribute prediction and 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
• Predicting structural classes of proteins by incorporating their global and local physicochemical and conformational properties into general Chou's PseAAC
• iMethyl-STTNC: identification of N\(^6\)-methyladenosine sites by extending the idea of SAAC into Chou's PseAAC to formulate RNA sequences
• Using the concept of Chou's pseudo amino acid composition for risk type prediction of human papillomaviruses
• 2D-MH: a web-server for generating graphic representation of protein sequences based on the physicochemical properties of their constituent amino acids
• Prediction of protein subcellular localization with oversampling approach and Chou's general PseAAC
• Prediction of GABA\(_{\mathrm A}\) receptor proteins using the concept of Chou's pseudo-amino acid composition and support vector machine
• The disposition of the LZCC protein residues in wenxiang diagram provides new insights into the protein-protein interaction mechanism
• Using LogitBoost classifier to predict protein structural classes
• An application of gene comparative image for predicting the effect on replication ratio by HBV virus gene missense mutation
• Using Chou's amphiphilic pseudo-amino acid composition and support vector machine for prediction of enzyme subfamily classes
• Prediction of \(\beta\)-lactamase and its class by Chou's pseudo-amino acid composition and support vector machine
• Chou's pseudo amino acid composition improves sequence-based antifreeze protein prediction
• Predicting anticancer peptides with Chou's pseudo amino acid composition and investigating their mutagenicity via ames test
• Bio-entity network for analysis of protein-protein interaction networks
• Random forests

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