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Special Issue

Data-Driven Discovery: Biostatistics, Biometrics & Computational Science

A JBRES Special Issue dedicated to rigorous, outcome-oriented advances in data-driven biomedical science—linking biostatistics, biometrics, computational modeling, AI/machine learning, and bioinformatics to enhance discovery, reproducibility, and translational impact in health research.

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JBRES Special Issue • Biostatistics, Biometrics & Computational Science
Special Issue Title

Data-Driven Discovery: Biostatistics, Biometrics & Computational Science

This Special Issue of the Journal of Biomedical Research & Environmental Sciences (JBRES) invites high-quality submissions that strengthen the evidence base for data-driven methods in biomedical and health sciences. We welcome work that advances statistical modeling & biometrics, develops computational tools & AI applications, and demonstrates reproducibility & real-world impact across omics, clinical trials, and population health.

Biostatistics & Biometrics
Advanced statistical methods, survival analysis, Bayesian approaches, biomarker validation, imaging biometrics, high-dimensional data analysis.
Computational & AI Tools
Machine learning, bioinformatics pipelines, multi-omics integration, big data platforms, reproducible workflows, AI-driven discovery.
Submit to This Special Issue Submission Guidelines Peer Review Policy

About this Special Issue

Biomedical research is strongest when it connects robust data analysis with innovative computational methods and reproducible outcomes. This Special Issue focuses on data-driven discovery—from biostatistics and biometrics to computational modeling, machine learning applications, and bioinformatics tools. We especially welcome submissions that report transparent methods, practical endpoints, and insights that can improve discovery, precision medicine, and long-term health impact.

Preference is given to studies with clear outcomes (model performance, validation metrics, biological insights), robust analysis, and a concise “What this changes in practice” statement.

Topics of interest

Submissions may address (but are not limited to):

Advanced Biostatistics & Modeling Biometrics & Biomarker Validation Machine Learning in Biomedicine Bioinformatics & Omics Analysis Big Data & Multi-Omics Integration AI-Driven Drug Discovery & Modeling Reproducible Computational Workflows Statistical Methods for Clinical Trials Bayesian & High-Dimensional Statistics Computational Precision Medicine Data Science in Population Health Imaging & Digital Biometrics

Article types accepted

  • Original Research Articles
  • Clinical Trials & Intervention Studies
  • Observational & Population Studies
  • Review Articles
  • Systematic Reviews & Meta-Analyses
  • Mini-Reviews
  • Short Communications
  • Case Studies (where appropriate)
Tip: Include (1) primary/secondary outcomes, (2) a brief methods transparency note (preregistration/data availability if applicable), and (3) a “Clinical/Community Relevance” paragraph.

Why submit to this Special Issue?

  • Data-forward visibility: your work is presented alongside leading biostatistics, biometrics, and computational science research.
  • Efficient editorial pathway: JBRES targets rapid peer review (7–14 days where applicable).
  • Permanent DOI: accepted articles receive DOI for strong citation continuity.
  • Open access reach: broad discoverability for clinicians, researchers, and data scientists.
  • Author support: clear guidance through review, proofing, and publication.
Ready to submit?
Submit online, or share an abstract for an editorial scope check (methods + outcomes summary preferred).
Submit Manuscript Email Abstract
Submission Portal Submission Instructions Aims & Scope Archive
Alternative submission emails: support@jelsciences.com | editor.review@scireslit.us
License (upon publication): Creative Commons CC BY 4.0

Co-Guest Editor

Yingjun Zhao

Yingjun Zhao

Co-Guest Editor

Affiliation: Department of Intelligent Manufacturing Engineering, Xinjiang University

Location: China

Email: 107552404325@stu.xju.edu.cn

Research Focus: Intelligent manufacturing, fault diagnosis, condition monitoring, deep learning applications, signal processing, and industrial big data analytics.

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