BlaPred: predicting and classifying \(\beta\)-lactamase using a 3-tier prediction system via Chou's general PseAAC

DOI10.1016/J.JTBI.2018.08.030zbMath1406.92215OpenAlexW2888551041WikidataQ91107197 ScholiaQ91107197MaRDI QIDQ1712641

Peng Zhang

Publication date: 31 January 2019

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

Full work available at URL: https://doi.org/10.1016/j.jtbi.2018.08.030

zbMATH Keywords

support vector machineantibiotic resistancepseudo amino acid composition\(\beta\)-lactamase\textit{leave-one-out} cross-validation

Mathematics Subject Classification ID

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

Related Items (4)

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 ⋮ BlaPred

Uses Software

• LogitBoost
• PseAAC
• PredLactamase
• pSuc-Lys
• Cd-hit
• iEnhancer-2L
• UniProt
• PseAAC-Builder
• Pse-in-One
• GPCR-2L
• iPro54-PseKNC
• iPPI-Esml
• iAMP-2L
• PseKNC
• iRSpot-PseDNC
• iSNO-AAPair
• iTIS-PseTNC
• iNitro-Tyr
• Quat-2L
• iPTM-mLys
• pLoc-mAnimal
• pLoc-mVirus
• pLoc-mEuk
• iRNA-PseColl
• iATC-mHyb
• iRNAm5C-PseDNC
• POSSUM
• iPreny-PseAAC
• iRSpot-EL
• iPromoter-2L
• pLoc-mGneg
• pLoc-mPlant
• NR-2L
• iProt-Sub
• iRNA-PseU
• iRNA-3typeA
• pLoc-mHum
• iDNA6mA-PseKNC
• 2L-piRNA
• PROSPERous

Cites Work

• pSuc-Lys: predict lysine succinylation sites in proteins with PseAAC and ensemble random forest approach
• 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
• Prediction of protein subcellular localization with oversampling approach and Chou's general PseAAC
• Using LogitBoost classifier to predict protein structural classes
• Prediction of \(\beta\)-lactamase and its class by Chou's pseudo-amino acid composition and support vector machine
• Discrimination of acidic and alkaline enzyme using Chou's pseudo amino acid composition in conjunction with probabilistic neural network model
• Gram-positive and Gram-negative protein subcellular localization by incorporating evolutionary-based descriptors into Chou's general PseAAC

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