Accurate in silico identification of protein succinylation sites using an iterative semi-supervised learning technique

DOI10.1016/J.JTBI.2015.03.029zbMath1341.92023OpenAlexW2046534253WikidataQ41114428 ScholiaQ41114428MaRDI QIDQ2630310

Xiaowei Zhao, Qiao Ning, Haiting Chai, Zhiqiang Ma

Publication date: 27 July 2016

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

Full work available at URL: https://doi.org/10.1016/j.jtbi.2015.03.029

zbMATH Keywords

multiple featurespositive samples only learningsuccinylated proteins

Mathematics Subject Classification ID

Biochemistry, molecular biology (92C40) Software, source code, etc. for problems pertaining to biology (92-04)

Related Items (2)

pSuc-Lys: predict lysine succinylation sites in proteins with PseAAC and ensemble random forest approach ⋮ PSSM-Suc: accurately predicting succinylation using position specific scoring matrix into bigram for feature extraction

Uses Software

• LIBSVM
• Cell-PLoc
• Cd-hit
• iSNO-PseAAC
• PSoL
• GPS-YNO2
• PLMLA

Cites Work

• Unnamed Item

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