From Biostatistics to Machine Learning: Modern Approaches in Bioengineering for Modeling and Understanding Biological Complexity
A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".
Deadline for manuscript submissions: 1 December 2025 | Viewed by 661
Special Issue Editors
Interests: biostatistics; statistical genetics; bioinformatics; omics data analysis; proteomics; metabolomics; machine learning; integrative analysis; medical data analysis; computational biology
Special Issues, Collections and Topics in MDPI journals
Interests: biostatistics; clinical trial; controlled clinical trial; data accuracy; data interpretation, statistical; high dimensional data analysis; historical data for clinical trials; machine learning; missing data; right-censored data analysis
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Biostatistics and machine learning have become increasingly vital as researchers aim to decode and manipulate complex biological systems. This Special Issue seeks to highlight innovative statistical methods and machine learning tools that advance our understanding of biological processes and enhance the design, modeling, and optimization of bioengineering applications.
We welcome original research, methodological papers, and review articles that showcase how cutting-edge statistical and machine learning approaches contribute to solving challenges in bioengineering.
Scope and Topics of Interest (include but are not limited to):
- Statistical modeling of multi-omics and high-dimensional biological data;
- Bayesian methods in synthetic biology and bioengineering;
- Machine learning and AI-driven tools in bioengineering;
- Experimental design and optimization for bioprocessing;
- Spatial and temporal modeling in tissue engineering;
- Causal inference in biological networks;
- Uncertainty quantification in biological system modeling;
- Integration of statistics with mechanistic modeling;
- Predictive modeling for biomanufacturing and drug delivery systems.
- Applications of statistical genomics in bioengineering contexts.
Dr. Chien-Wei (Masaki) Lin
Prof. Dr. Kwang Woo Ahn
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Bioengineering is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- statistics
- machine learning
- AI
- computational tools
- prediction and classification
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