Fu-SulfPred: identification of protein S-sulfenylation sites by fusing forests via Chou's general PseAAC

DOI10.1016/J.JTBI.2018.10.046zbMath1406.92221OpenAlexW2896821612WikidataQ58575869 ScholiaQ58575869MaRDI QIDQ1716873

Yashuang Mu, Lidong Wang, Ruijun Zhang

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.2018.10.046

zbMATH Keywords

predictionsamplingfeature extractiondecision treesforestS-sulfenylation

Mathematics Subject Classification ID

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) Software, source code, etc. for problems pertaining to biology (92-04)

Related Items (3)

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 ⋮ Fu-SulfPred

Uses Software

• C4.5
• pSuc-Lys
• Pse-in-One
• iPro54-PseKNC
• iRSpot-TNCPseAAC
• iPPI-Esml
• Prnam-PC
• iDNA-Methyl
• iPPBS-Opt
• iSuc-PseOpt
• iRSpot-PseDNC
• iRNA-Methyl
• iMethyl-PseAAC
• iSNO-PseAAC
• iNitro-Tyr
• iPTM-mLys
• pSumo-CD
• iCar-PseCp
• iHyd-PseCp
• iPhos-PseEvo
• iPhos-PseEn
• pLoc-mEuk
• iRNA-PseColl
• iRNA-2methyl
• iRNAm5C-PseDNC
• iPreny-PseAAC
• NucPosPred
• iRSpot-EL
• iPromoter-2L
• pLoc-mGneg
• iKcr-PseEns
• iROS-gPseKNC
• iProt-Sub
• iRNA-PseU
• iRNA-3typeA
• iDNA6mA-PseKNC
• 2L-piRNA
• iEnhancer-EL
• iRO-3wPseKNC
• iUbiq-Lys
• iHyd-PseAAC
• iLoc-lncRNA
• XGBFEMF

Cites Work

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• pSuc-Lys: predict lysine succinylation sites in proteins with PseAAC and ensemble random forest approach
• NucPosPred: predicting species-specific genomic nucleosome positioning via four different modes of general PseKNC
• 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
• pLoc\_bal-mGneg: predict subcellular localization of Gram-negative bacterial proteins by quasi-balancing training dataset and general PseAAC

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