OptimDase: An Algorithm for Predicting DNA Binding Sites with Combined Feature Encoding.

Zhendong Liu, Jun S Liu, Dongqing Wei, Rongjun Man, Jiamin Jiang, Bofeng Zhang, Liping Li, Zhiyong Zhao

Abstract read

In one paragraph

Article in Interdisciplinary sciences, computational life sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

MegSite: an accurate nucleic acid-binding residue prediction method based on multimodal protein language model.Briefings in bioinformatics · 2025
Article

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Zhendong LiuSchool of Computer and Information Engineering, Shanghai Polytechnic University, Shanghai, 201209, China. liuzd2000@126.com.

Jun S LiuDepartment of Statistics, Harvard University, Cambridge, 02138, USA. jliu@stat.harvard.edu.

Dongqing WeiState Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200030, P.R. China.

Rongjun ManDepartment of Otolaryngology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, 250021, China. manrongjun@sdfmu.edu.cn.

Jiamin JiangSchool of Computer and Information Engineering, Shanghai Polytechnic University, Shanghai, 201209, China.

Bofeng ZhangSchool of Computer and Information Engineering, Shanghai Polytechnic University, Shanghai, 201209, China.

Liping LiSchool of Computer and Information Engineering, Shanghai Polytechnic University, Shanghai, 201209, China.

Zhiyong ZhaoDepartment of Medical Imaging, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, 250021, China.

Funding

National Natural Science Foundation of China 61672328

6 · The paper itself

Abstract

Identifying DNA binding sites remains a critical task in bioinformatics, with applications ranging from gene regulation studies to drug design. Although progress has been made in computational techniques, we still face challenges such as data complexity and prediction accuracy. In this paper, we introduce OptimDase, a new algorithm. It integrates feature encoding with optimum decision-making frameworks to improve DNA binding site prediction. OptimDase integrates multi-scale scanning and feature selection strategies, making it highly effective for both classification and regression tasks. Our experiments demonstrate that OptimDase achieves superior performance with an accuracy of 0.8943 in classification tasks and an RMSE of 0.0054 in regression tasks, outperforming existing algorithms in key evaluation metrics. These results highlight OptimDase's portability and robustness, making it an effective solution for identifying DNA binding sites and advancing the applications of drug design.

Indexed as

AlgorithmsComputational BiologyDNABinding SitesDNAcombined feature encodingDNA binding siteMachine learningoptimum decision-makingSite-specific recombination

Identifiers

PMID40495055

DOI10.1007/s12539-025-00704-8

PMCPMC12672825

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LicenceCC BY

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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.