pSSbond-PseAAC: prediction of disulfide bonding sites by integration of PseAAC and statistical moments
From MaRDI portal
Publication:1717058
DOI10.1016/j.jtbi.2018.12.015zbMath1406.92460OpenAlexW2905397045WikidataQ90503965 ScholiaQ90503965MaRDI QIDQ1717058
Nouman Rasool, Yaser Daanial Khan, Kuo-Chen Chou, Waqar Hussain, Mehreen Jamil, Sher Afzal Khan
Publication date: 6 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.2018.12.015
Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Learning and adaptive systems in artificial intelligence (68T05) Protein sequences, DNA sequences (92D20)
Related Items
Uses Software
- pSuc-Lys
- iEnhancer-2L
- propy
- Pse-in-One
- iDNA-Prot
- iPro54-PseKNC
- Prnam-PC
- iDHS-EL
- iSuc-PseOpt
- iRSpot-PseDNC
- iMethyl-PseAAC
- iSNO-AAPair
- iPTM-mLys
- iPhos-PseEvo
- iRNA-AI
- pLoc-mAnimal
- pLoc-mVirus
- pLoc-mEuk
- iRNA-PseColl
- iATC-mHyb
- iRNAm5C-PseDNC
- iPreny-PseAAC
- DPP-PseAAC
- iRSpot-EL
- iPromoter-2L
- pLoc-mGneg
- pLoc-mPlant
- iKcr-PseEns
- iProt-Sub
- iRNA-PseU
- pLoc_bal-mHum
- iPPI-PseAAC
- 2L-piRNA
- pLoc_bal-mGpos
- iHyd-PseAAC
- BlaPred
- pLoc_bal-mGneg
Cites Work
- pSuc-Lys: predict lysine succinylation sites in proteins with PseAAC and ensemble random forest approach
- Naïve Bayes classifier with feature selection to identify phage virion proteins
- Predicting protein submitochondrial locations by incorporating the pseudo-position specific scoring matrix into the general Chou's pseudo-amino acid composition
- Identifying 5-methylcytosine sites in RNA sequence using composite encoding feature into Chou's 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
- BlaPred: predicting and classifying \(\beta\)-lactamase using a 3-tier prediction system via Chou's general PseAAC
- Predicting apoptosis protein subcellular localization by integrating auto-cross correlation and PSSM into Chou's PseAAC
- pLoc\_bal-mGneg: predict subcellular localization of Gram-negative bacterial proteins by quasi-balancing training dataset and general PseAAC
- Identify Gram-negative bacterial secreted protein types by incorporating different modes of PSSM into Chou's general PseAAC via Kullback-Leibler divergence
- Predicting structural classes of proteins by incorporating their global and local physicochemical and conformational properties into general Chou's PseAAC
- iMethyl-STTNC: identification of N\(^6\)-methyladenosine sites by extending the idea of SAAC into Chou's PseAAC to formulate RNA sequences
- Predicting membrane protein types by incorporating a novel feature set into Chou's general PseAAC
- Analysis and prediction of ion channel inhibitors by using feature selection and Chou's general pseudo amino acid composition
- iPPI-PseAAC(CGR): identify protein-protein interactions by incorporating chaos game representation into PseAAC
- Prediction of protein subcellular localization with oversampling approach and Chou's general PseAAC
- Methods to predict protein spatial structure
