PSSM-Suc: accurately predicting succinylation using position specific scoring matrix into bigram for feature extraction

DOI10.1016/j.jtbi.2017.05.005zbMath1381.92002OpenAlexW2611495100WikidataQ47854726 ScholiaQ47854726MaRDI QIDQ1701598

Sunil Pranit Lal, Tatsuhiko Tsunoda, Abdul Sattar, Abdollah Dehzangi, Ghazaleh Taherzadeh, Yosvany López, Jacob Michaelson, Alok Sharma

Publication date: 27 February 2018

Published in: Journal of Theoretical Biology (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jtbi.2017.05.005

zbMATH Keywords

amino acidsprotein sequencessuccinylation prediction

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Biochemistry, molecular biology (92C40) Computational methods for problems pertaining to biology (92-08)

Related Items (1)

MFSC: multi-voting based feature selection for classification of Golgi proteins by adopting the general form of Chou's PseAAC components

Uses Software

• WEKA
• Cell-PLoc
• PSIPRED
• C4.5
• pSuc-Lys
• Cd-hit
• PSI-BLAST
• BLAST
• iDrug-Target
• iDNA-Methyl
• iSuc-PseOpt
• iRNA-Methyl
• iCTX-Type
• iMethyl-PseAAC
• iUbiq-Lys
• SuccFind

Cites Work

• Unnamed Item
• pSuc-Lys: predict lysine succinylation sites in proteins with PseAAC and ensemble random forest approach
• Some remarks on protein attribute prediction and pseudo amino acid composition
• A feature extraction technique using bi-gram probabilities of position specific scoring matrix for protein fold recognition
• Gram-positive and Gram-negative protein subcellular localization by incorporating evolutionary-based descriptors into Chou's general PseAAC
• Accurate in silico identification of protein succinylation sites using an iterative semi-supervised learning technique
• Predicting anticancer peptides with Chou's pseudo amino acid composition and investigating their mutagenicity via ames test

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