Prediction of S-sulfenylation sites using mRMR feature selection and fuzzy support vector machine algorithm
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Publication:1712633
DOI10.1016/J.JTBI.2018.08.022zbMath1406.92190OpenAlexW2886211322WikidataQ91040547 ScholiaQ91040547MaRDI QIDQ1712633
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.2018.08.022
Learning and adaptive systems in artificial intelligence (68T05) Biochemistry, molecular biology (92C40)
Uses Software
- pSuc-Lys
- iLM-2L
- iEnhancer-2L
- Pse-in-One
- Prnam-PC
- iSuc-PseOpt
- iRNA-Methyl
- iMethyl-PseAAC
- iSNO-AAPair
- iNitro-Tyr
- iPTM-mLys
- iACP
- iPhos-PseEvo
- iPhos-PseEn
- pLoc-mAnimal
- pLoc-mEuk
- iRNA-PseColl
- iRNA-2methyl
- iRSpot-EL
- iPromoter-2L
- AAindex
- iRNA-PseU
- iRNA-3typeA
- iDNA6mA-PseKNC
- 2L-piRNA
- iEnhancer-EL
- iRO-3wPseKNC
- iRSpot-Pse6NC
- iLoc-lncRNA
Cites Work
- pSuc-Lys: predict lysine succinylation sites in proteins with PseAAC and ensemble random forest approach
- iLM-2L: a two-level predictor for identifying protein lysine methylation sites and their methylation degrees by incorporating K-gap amino acid pairs into Chou's general PseAAC
- Some remarks on protein attribute prediction and pseudo amino acid composition
- Fuzzy KNN for predicting membrane protein types from pseudo-amino acid composition
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