Predicting membrane protein types by incorporating a novel feature set into Chou's general PseAAC
From MaRDI portal
Publication:1714327
DOI10.1016/j.jtbi.2018.07.032zbMath1406.92470OpenAlexW2884824743WikidataQ57467559 ScholiaQ57467559MaRDI QIDQ1714327
E. Siva Sankari, D. Manimegalai
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.2018.07.032
decision tree classifier2-gram exchange groupexchange group local patterninterval patternmembrane protein types prediction
Biochemistry, molecular biology (92C40) Protein sequences, DNA sequences (92D20) Cell biology (92C37)
Related Items
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
Uses Software
- Memtype-2L
- pSuc-Lys
- iEnhancer-2L
- Pse-in-One
- iPro54-PseKNC
- iDrug-Target
- iAMP-2L
- Prnam-PC
- iDHS-EL
- iPPBS-Opt
- iSuc-PseOpt
- iLoc-Hum
- PseKNC
- iPTM-mLys
- pSumo-CD
- pLoc-mAnimal
- pLoc-mVirus
- pLoc-mEuk
- iRNA-PseColl
- iATC-mHyb
- iRSpot-EL
- iPromoter-2L
- PREvaIL
- pLoc-mGneg
- pLoc-mPlant
- MemHyb
- iProt-Sub
- iRNA-3typeA
- pLoc-mHum
- iDNA6mA-PseKNC
- 2L-piRNA
- iRSpot-Pse6NC
Cites Work
- pSuc-Lys: predict lysine succinylation sites in proteins with PseAAC and ensemble random forest approach
- Classification of membrane protein types using voting feature interval in combination with Chou's pseudo amino acid composition
- SLLE for predicting membrane protein types
- Geometry preserving projections algorithm for predicting membrane protein types
- 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
- Predicting membrane protein types by incorporating protein topology, domains, signal peptides, and physicochemical properties into the general form of Chou's pseudo amino acid composition
- Predicting membrane protein type by functional domain composition and pseudo-amino acid composition
- Using stacked generalization to predict membrane protein types based on pseudo-amino acid composition
- Fuzzy KNN for predicting membrane protein types from pseudo-amino acid composition
- Prediction of membrane protein types from sequences and position-specific scoring matrices
- MemHyb: predicting membrane protein types by hybridizing SAAC and PSSM
- Application of density similarities to predict membrane protein types based on pseudo-amino acid composition
- A two-stage SVM method to predict membrane protein types by incorporating amino acid classifications and physicochemical properties into a general form of Chou's PseAAC
