predForm-Site: Formylation site prediction by incorporating multiple features and resolving data imbalance

Md Khaled Ben Islam; Julia Rahman; Md Al Mehedi Hasan; Shamim Ahmad

doi:10.1016/j.compbiolchem.2021.107553

predForm-Site: Formylation site prediction by incorporating multiple features and resolving data imbalance

Comput Biol Chem. 2021 Oct:94:107553. doi: 10.1016/j.compbiolchem.2021.107553. Epub 2021 Jul 30.

Authors

Md Khaled Ben Islam¹, Julia Rahman², Md Al Mehedi Hasan³, Shamim Ahmad⁴

Affiliations

¹ Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, Australia; Department of Computer Science & Engineering, Pabna University of Science and Technology, Pabna, Bangladesh. Electronic address: mdkhaledben@gmail.com.
² Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, Australia; Department of Computer Science & Engineering, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh. Electronic address: julia.rahman@griffithuni.edu.au.
³ Department of Computer Science & Engineering, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh.
⁴ Department of Computer Science & Engineering, Rajshahi University, Rajshahi, Bangladesh.

PMID: 34384997
DOI: 10.1016/j.compbiolchem.2021.107553

Abstract

Formylation 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.

Keywords: Data imbalance issue; Feature integration; Lysine formylation sites prediction; Support vector machine.