pLoc\_bal-mGneg: predict subcellular localization of Gram-negative bacterial proteins by quasi-balancing training dataset and general PseAAC
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Publication:1712835
DOI10.1016/j.jtbi.2018.09.005zbMath1406.92173OpenAlexW2891918406WikidataQ91367801 ScholiaQ91367801MaRDI QIDQ1712835
Xiang Cheng, Kuo-Chen Chou, Xuan Xiao
Publication date: 1 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.2018.09.005
Applications of statistics to biology and medical sciences; meta analysis (62P10) Biochemistry, molecular biology (92C40) Cell biology (92C37)
Related Items (16)
The preliminary efficacy evaluation of the CTLA-4-ig treatment against lupus nephritis through \textit{in-silico} analyses ⋮ Prediction of antioxidant proteins by incorporating statistical moments based features into Chou's PseAAC ⋮ Predicting apoptosis protein subcellular localization by integrating auto-cross correlation and PSSM into 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 ⋮ Effective DNA binding protein prediction by using key features via Chou's general PseAAC ⋮ iPPI-PseAAC(CGR): identify protein-protein interactions by incorporating chaos game representation into PseAAC ⋮ Fu-SulfPred: identification of protein S-sulfenylation sites by fusing forests via Chou's general PseAAC ⋮ pSSbond-PseAAC: prediction of disulfide bonding sites by integration of PseAAC and statistical moments ⋮ MFSC: multi-voting based feature selection for classification of Golgi proteins by adopting the general form of Chou's PseAAC components ⋮ Analysis and prediction of animal toxins by various Chou's pseudo components and reduced amino acid compositions ⋮ Predicting protein-protein interactions by fusing various Chou's pseudo components and using wavelet denoising approach ⋮ iRNA-PseKNC(2methyl): identify RNA 2'-O-methylation sites by convolution neural network and Chou's pseudo components ⋮ SPrenylC-PseAAC: a sequence-based model developed via Chou's 5-steps rule and general PseAAC for identifying S-prenylation sites in proteins ⋮ Dforml(KNN)-PseAAC: detecting formylation sites from protein sequences using K-nearest neighbor algorithm via Chou's 5-step rule and pseudo components ⋮ pLoc_bal-mGneg ⋮ Predicting S-nitrosylation proteins and sites by fusing multiple features
Uses Software
- LogitBoost
- Cell-PLoc
- Psortb
- PredLactamase
- Memtype-2L
- pSuc-Lys
- iEnhancer-2L
- propy
- iDNA-Prot
- iPro54-PseKNC
- iRSpot-TNCPseAAC
- iPPI-Esml
- iDrug-Target
- Prnam-PC
- iDNA-Methyl
- iLoc-Animal
- iPPBS-Opt
- iSuc-PseOpt
- PseKNC
- iRSpot-PseDNC
- iSS-PseDNC
- iRNA-Methyl
- iCTX-Type
- AFP-Pred
- GOASVM
- iSNO-PseAAC
- iSNO-AAPair
- iTIS-PseTNC
- iNitro-Tyr
- iNuc-PhysChem
- Pse-analysis
- iPTM-mLys
- pSumo-CD
- iACP
- iCar-PseCp
- iHyd-PseCp
- iPhos-PseEn
- iRNA-AI
- pLoc-mAnimal
- iRNA-PseColl
- iATC-mHyb
- iRNAm5C-PseDNC
- POSSUM
- iRSpot-EL
- iPromoter-2L
- PREvaIL
- Unb-DPC
- OOgenesis_Pred
- pLoc-mGneg
- iHSP-PseRAAAC
- iKcr-PseEns
- iNuc-STNC
- Gneg-mPLoc
- iNR-PhysChem
- iProt-Sub
- iRNA-PseU
- iRNA-3typeA
- pLoc-mHum
- iDNA6mA-PseKNC
- Quokka
- 2L-piRNA
- iEnhancer-EL
- iRO-3wPseKNC
- iRSpot-Pse6NC
- WoLF PSORT
- iFeature
- PROSPERous
- SMOTE
Cites Work
- Unnamed Item
- Predicting plant protein subcellular multi-localization by Chou's PseAAC formulation based multi-label homolog knowledge transfer learning
- pSuc-Lys: predict lysine succinylation sites in proteins with PseAAC and ensemble random forest approach
- Prediction of protein structure classes by incorporating different protein descriptors into general Chou's pseudo amino acid composition
- Classification of membrane protein types using voting feature interval in combination with Chou's pseudo amino acid composition
- Use of fuzzy clustering technique and matrices to classify amino acids and its impact to Chou's pseudo amino acid composition
- A novel feature representation method based on Chou's pseudo amino acid composition for protein structural class prediction
- 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
- Using the concept of Chou's pseudo amino acid composition for risk type prediction of human papillomaviruses
- Gneg-mPLoc: a top-down strategy to enhance the quality of predicting subcellular localization of Gram-negative bacterial proteins
- Prediction of protein subcellular localization with oversampling approach and Chou's general PseAAC
- Prediction of GABA\(_{\mathrm A}\) receptor proteins using the concept of Chou's pseudo-amino acid composition and support vector machine
- Predicting mycobacterial proteins subcellular locations by incorporating pseudo-average chemical shift into the general form of Chou's pseudo amino acid composition
- Predicting protein structural classes with pseudo amino acid composition: an approach using geometric moments of cellular automaton image
- Using LogitBoost classifier to predict protein structural classes
- Using Chou's amphiphilic pseudo-amino acid composition and support vector machine for prediction of enzyme subfamily classes
- GOASVM: a subcellular location predictor by incorporating term-frequency gene ontology into the general form of Chou's pseudo-amino acid composition
- Prediction of \(\beta\)-lactamase and its class by Chou's pseudo-amino acid composition and support vector machine
- Gram-positive and Gram-negative protein subcellular localization by incorporating evolutionary-based descriptors into Chou's general PseAAC
- Chou's pseudo amino acid composition improves sequence-based antifreeze protein prediction
- Predicting anticancer peptides with Chou's pseudo amino acid composition and investigating their mutagenicity via ames test
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