iSuc-PseOpt statistics

Tool stats & trends

Looking to identify usage trends or leading experts?

iSuc-PseOpt specifications


Unique identifier OMICS_11163
Name iSuc-PseOpt
Interface Web user interface
Restrictions to use None
Input data Protein sequence(s)
Input format FASTA
Computer skills Basic
Stability Stable
Maintained Yes


  • person_outline Xuan Xiao

Publication for iSuc-PseOpt

iSuc-PseOpt citations


Improving succinylation prediction accuracy by incorporating the secondary structure via helix, strand and coil, and evolutionary information from profile bigrams

PLoS One
PMCID: 5809022
PMID: 29432431
DOI: 10.1371/journal.pone.0191900

[…] uld not be computed, that of SSEvol-Suc was calculated for 6-, 8- and 10-fold cross-validations.As shown in , SSEvol-Suc represents a significant improvement over the four predictors: iSuc-PseAAC [], iSuc-PseOpt [], SuccinSite [] and pSuc-Lys []. SSEvol-Suc outperformed the previous predictors in statistics such as sensitivity, accuracy and MCC. For instance, sensitivity, accuracy and MCC signific […]


Success: evolutionary and structural properties of amino acids prove effective for succinylation site prediction

BMC Genomics
PMCID: 5781056
PMID: 29363424
DOI: 10.1186/s12864-017-4336-8

[…] We compared the Success predictor with three recently proposed predictors, namely, iSuc-PseOpt [], SuccinSite [] and pSuc-Lys []. These predictors are available as active web servers to which any protein sequence can be uploaded for succinylation site identification. It is worth not […]


A systematic identification of species specific protein succinylation sites using joint element features information

PMCID: 5584904
PMID: 28894368
DOI: 10.2147/IJN.S140875

[…] tor SuccinSite, in which the three informative sequence encoding features, that is, CKSAAP, binary, and the selected AAindex physicochemical features, were combined. Recently, Jia et al developed the iSuc-PseOpt predictor, based on pseudo amino acid composition encoding with K-nearest neighbors’ algorithm. Meanwhile, Jia et al developed pSuc-Lys based on a pseudo amino acid composition encoding an […]


Looking to check out a full list of citations?

iSuc-PseOpt institution(s)
Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen, China; Gordon Life Science Institute, Boston, MA, USA; Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah, Saudi Arabia
iSuc-PseOpt funding source(s)
This work was partially supported by the National Nature Science Foundation of China (61261027, 31260273, 31560316, and 31560316), the Natural Science Foundation of Jiangxi Province (20122BAB211033, 20122BAB201044, and 20132BAB201053), and the Scientific Research plan of the Department of Education of Jiangxi Province (GJJ14640).

iSuc-PseOpt reviews

star_border star_border star_border star_border star_border
star star star star star

Be the first to review iSuc-PseOpt