iMethyl-STTNC: identification of N\(^6\)-methyladenosine sites by extending the idea of SAAC into Chou's PseAAC to formulate RNA sequences
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Publication:1714298
DOI10.1016/J.JTBI.2018.07.018zbMath1406.92448OpenAlexW2884524796WikidataQ57467581 ScholiaQ57467581MaRDI QIDQ1714298
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.2018.07.018
Learning and adaptive systems in artificial intelligence (68T05) Biochemistry, molecular biology (92C40) Protein sequences, DNA sequences (92D20)
Related Items (5)
iPPI-PseAAC(CGR): identify protein-protein interactions by incorporating chaos game representation into PseAAC ⋮ Prediction and functional analysis of prokaryote lysine acetylation site by incorporating six types of features into Chou's general PseAAC ⋮ pSSbond-PseAAC: prediction of disulfide bonding sites by integration of PseAAC and statistical moments ⋮ 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
- Cell-PLoc
- iNuc-PseKNC
- Pse-in-One
- iPro54-PseKNC
- iRSpot-TNCPseAAC
- iPPI-Esml
- iSS-Hyb-mRMR
- iAMP-2L
- iDNA-Methyl
- iMiRNA-PseDPC
- iSuc-PseOpt
- PseAAC-General
- SubMito-PSPCP
- iLoc-Hum
- iLoc-Euk
- PseKNC
- iRNA-Methyl
- iCTX-Type
- iMethyl-PseAAC
- iSNO-PseAAC
- iNitro-Tyr
- iNuc-PhysChem
- iPTM-mLys
- iPhos-PseEn
- pLoc-mEuk
- iRNA-PseColl
- iRNA-2methyl
- iRNAm5C-PseDNC
- pLoc-mPlant
- MemHyb
- TargetFreeze
- iNR-PhysChem
- iRNA-PseU
- pLoc-mHum
- iDNA6mA-PseKNC
- PSOFuzzySVM-TMH
- 2L-piRNA
- iUbiq-Lys
- iHyd-PseAAC
- PSNO
Cites Work
- Classification of membrane protein types using voting feature interval in combination with Chou's pseudo amino acid composition
- Machine learning based adaptive watermark decoding in view of anticipated attack
- Predicting protein submitochondrial locations by incorporating the pseudo-position specific scoring matrix into the general Chou's pseudo-amino acid composition
- 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
- Predicting membrane protein types by fusing composite protein sequence features into 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
- Support-vector networks
- MemHyb: predicting membrane protein types by hybridizing SAAC and PSSM
- Discrimination of acidic and alkaline enzyme using Chou's pseudo amino acid composition in conjunction with probabilistic neural network model
- Statistics of local complexity in amino acid sequences and sequence databases
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