Predicting apoptosis protein subcellular localization by integrating auto-cross correlation and PSSM into Chou's PseAAC

DOI10.1016/J.JTBI.2018.08.042zbMath1406.92230OpenAlexW2889111282WikidataQ91282446 ScholiaQ91282446MaRDI QIDQ1712667

Yunyun Liang, Sheng-Li Zhang

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.2018.08.042

zbMATH Keywords

autocorrelation functioncross correlationsupport vector machineprotein subcellular localizationPSSM

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Learning and adaptive systems in artificial intelligence (68T05) Biochemistry, molecular biology (92C40) Protein sequences, DNA sequences (92D20) Cell biology (92C37)

Related Items (2)

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
• pSuc-Lys
• PISCES
• PSI-BLAST
• BLAST
• Pse-in-One
• iPro54-PseKNC
• iLoc-Euk
• PseKNC
• iRSpot-PseDNC
• iSS-PseDNC
• iNitro-Tyr
• iPTM-mLys
• iACP
• iOri-Human
• iRNA-AI
• pLoc-mAnimal
• pLoc-mVirus
• pLoc-mEuk
• iRNA-PseColl
• iRSpot-EL
• iPromoter-2L
• pLoc-mGneg
• pLoc-mPlant
• iHSP-PseRAAAC
• iRNA-3typeA
• pLoc-mHum
• iDNA6mA-PseKNC
• 2L-piRNA
• iEnhancer-EL
• iRO-3wPseKNC
• pLoc_bal-mGpos
• iRSpot-Pse6NC
• iLoc-lncRNA

Cites Work

• pSuc-Lys: predict lysine succinylation sites in proteins with PseAAC and ensemble random forest approach
• A novel representation for apoptosis protein subcellular localization prediction using support vector machine
• 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
• Prediction of the subcellular location of apoptosis proteins
• Prediction of apoptosis protein subcellular location using improved hybrid approach and pseudo-amino acid composition
• Gram-positive and Gram-negative protein subcellular localization by incorporating evolutionary-based descriptors into Chou's general PseAAC
• NOTES ON CONTINUOUS STOCHASTIC PHENOMENA

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