Evidence map›Paper›PMID 37702240›Full record

ArticleCombinatorial chemistry & high throughput screening2024

Investigating the Precise Identification of Citrullination Sites with High- Performance Score Metrics Using a Powerful Computation Predicting Tool.

Fee Faysal Ahmed, Anamika Podder, Md Farhad Bulbul, Md Amzad Hossain, Mahedi Hasan, Md Abdur Rauf Sarkar, Daijin Kim

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Article in Combinatorial chemistry & high throughput screening, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

7 authors.

Fee Faysal AhmedDepartment of Mathematics, Jashore University of Science and Technology, Jashore, 7408, Bangladesh.
Anamika PodderDepartment of Mathematics, Jashore University of Science and Technology, Jashore, 7408, Bangladesh.
Md Farhad BulbulDepartment of Mathematics, Jashore University of Science and Technology, Jashore, 7408, Bangladesh.
Md Amzad HossainDepartment of Electrical and Electronic Engineering, Jashore University of Science and Technology, Jashore -7408, Bangladesh.
Mahedi HasanDepartment of Computer Science and Engineering, Jashore University of Science and Technology, Jashore, 7408, Bangladesh.
Md Abdur Rauf SarkarDepartment of Genetic Engineering and Biotechnology, Jashore University of Science and Technology, Jashore 7408, Bangladesh.
Daijin KimDepartment of Computer Science & Engineering, Pohang University of Science and Technology (POSTECH), 77 Cheongam, Pohang 37673, Korea.

Funding

Institute of Information and Communications Technology Planning and Evaluation (IITP) by the Korean government (MSIT) 2017-0-00897, 2018-0-01290
6 · The paper itself

Abstract

backgroundTo elucidate the detailed mechanisms of citrullination at the molecular level and design drugs applicable to major human diseases, predicting protein citrullination sites (PCSs) is essential. Using experimental approaches to predict PCSs is time-consuming and costly. However, there is a limited scope of the current PCS predictors. In particular, most predictors are commonly used for PCS prediction and have limited performance scores.

objectiveThis work aims to provide an improved sophisticated predictor of citrullination sites using a benchmark dataset in a machine learning platform.

methodsThis study presents a reliable citrullination site predictor based on a benchmark dataset containing a 1:1 ratio of positive and negative samples. We classified citrullination sites using the Composition of the K-Spaced Amino Acid Pairs (CKSAAP) and Support Vector Machine (SVM).

resultsWe developed PCS predictors using integrated machine-learning methods that produced the highest average scores. Using 10-fold cross-validation on test datasets, the True Positive Rate (TPR) was 98.34%, the True Negative Rate (TNR) was 99.44%, the accuracy was 98.89%, the Mathew Correlation Coefficient (MCC) was 98.21%, the Area Under the ROC Curve (AUC) was 0.999, and the partial Area Under the ROC Curve (pAUC) was 0.1968.

conclusionAccording to overall performance, our developed predictor has a significantly higher implementation in comparison with the current tools on the same benchmark dataset. Moreover, it showed better performance metrics on both test and training datasets. Our developed predictor is promising and can be implemented as a complementary technique for identifying fast and precise citrullination sites.

Indexed as

CitrullinationSupport Vector MachineComputational BiologyHumansMachine Learningcitrullination sitefeatures encodingK-spaced amino acid pairs (CKSAAP)machine learning techniquesPost-translational modificationssupport vector machinesupport vector machine (SVM).

Identifiers

PMID37702240

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.