Prediction of protein subcellular localization with oversampling approach and Chou's general PseAAC
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Publication:1752399
DOI10.1016/J.JTBI.2017.10.030zbMath1394.92047OpenAlexW2766117835WikidataQ47443685 ScholiaQ47443685MaRDI QIDQ1752399
Publication date: 24 May 2018
Published in: Journal of Theoretical Biology (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.jtbi.2017.10.030
Learning and adaptive systems in artificial intelligence (68T05) Biochemistry, molecular biology (92C40)
Related Items (8)
Predicting protein submitochondrial locations by incorporating the pseudo-position specific scoring matrix into the general Chou's pseudo-amino acid composition ⋮ BlaPred: predicting and classifying \(\beta\)-lactamase using a 3-tier prediction system via Chou's general 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 ⋮ 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 ⋮ 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
- LIBSVM
- Pse-in-One
- iDrug-Target
- iLoc-Virus
- iLoc-Gpos
- iLoc-Plant
- iDNA-Methyl
- iLoc-Animal
- iPPBS-Opt
- iSuc-PseOpt
- iLoc-Hum
- iLoc-Euk
- Plant-mPLoc
- PseKNC
- iPhos-PseEvo
- iRNA-AI
- pLoc-mAnimal
- pLoc-mVirus
- pLoc-mEuk
- iRNA-PseColl
- iATC-mHyb
- iRNA-2methyl
- iRNAm5C-PseDNC
- POSSUM
- iPreny-PseAAC
- iRSpot-EL
- Unb-DPC
- OOgenesis_Pred
- pLoc-mPlant
- Gpos-mPLoc
- Gneg-mPLoc
- Hum-mPLoc
- 2L-piRNA
Cites Work
- Unnamed Item
- A novel representation for apoptosis protein subcellular localization prediction using support vector machine
- Some remarks on protein attribute prediction and pseudo amino acid composition
- Gneg-mPLoc: a top-down strategy to enhance the quality of predicting subcellular localization of Gram-negative bacterial proteins
- Evaluation of regular and singular domain integrals with boundary-only discretization-theory and Fortran code
- \textbf{iLoc-Virus}: a multi-label learning classifier for identifying the subcellular localization of virus proteins with both single and multiple sites
- Prediction of the subcellular location of apoptosis proteins
- Prediction of apoptosis protein subcellular location using improved hybrid approach and pseudo-amino acid composition
- A protein structural classes prediction method based on PSI-BLAST profile
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