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dc.contributor.authorIslam, Md Khaled Ben
dc.contributor.authorRahman, Julia
dc.contributor.authorHasan, Md Al Mehedi
dc.contributor.authorAhmad, Shamim
dc.date.accessioned2021-08-17T05:36:25Z
dc.date.available2021-08-17T05:36:25Z
dc.date.issued2021
dc.identifier.issn1476-9271
dc.identifier.doi10.1016/j.compbiolchem.2021.107553
dc.identifier.urihttp://hdl.handle.net/10072/407007
dc.description.abstractFormylation is one of the newly discovered post-translational modifications in lysine residue which is responsible for different kinds of diseases. In this work, a novel predictor, named predForm-Site, has been developed to predict formylation sites with higher accuracy. We have integrated multiple sequence features for developing a more informative representation of formylation sites. Moreover, decision function of the underlying classifier have been optimized on skewed formylation dataset during prediction model training for prediction quality improvement. On the dataset used by LFPred and Formator predictor, predForm-Site achieved 99.5% sensitivity, 99.8% specificity and 99.8% overall accuracy with AUC of 0.999 in the jackknife test. In the independent test, it has also achieved more than 97% sensitivity and 99% specificity. Similarly, in benchmarking with recent method CKSAAP_FormSite, the proposed predictor significantly outperformed in all the measures, particularly sensitivity by around 20%, specificity by nearly 30% and overall accuracy by more than 22%. These experimental results show that the proposed predForm-Site can be used as a complementary tool for the fast exploration of formylation sites. For convenience of the scientific community, predForm-Site has been deployed as an online tool, accessible at http://103.99.176.239:8080/predForm-Site.
dc.description.peerreviewedYes
dc.languageeng
dc.publisherElsevier BV
dc.relation.ispartofpagefrom107553
dc.relation.ispartofjournalComputational Biology and Chemistry
dc.relation.ispartofvolume94
dc.subject.fieldofresearchChemical sciences
dc.subject.fieldofresearchBiological sciences
dc.subject.fieldofresearchInformation and computing sciences
dc.subject.fieldofresearchcode34
dc.subject.fieldofresearchcode31
dc.subject.fieldofresearchcode46
dc.subject.keywordsData imbalance issue
dc.subject.keywordsFeature integration
dc.subject.keywordsLysine formylation sites prediction
dc.subject.keywordsSupport vector machine
dc.titlepredForm-Site: Formylation site prediction by incorporating multiple features and resolving data imbalance
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationIslam, MKB; Rahman, J; Hasan, MAM; Ahmad, S, predForm-Site: Formylation site prediction by incorporating multiple features and resolving data imbalance, Computational Biology and Chemistry 2021, 94, pp. 107553
dcterms.dateAccepted2021-07-28
dc.date.updated2021-08-15T23:42:07Z
gro.hasfulltextNo Full Text
gro.griffith.authorRahman, Julia
gro.griffith.authorIslam, Khaled Ben


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