Machine Learning Algorithms and Their Applications in Bioinformatics

Dear Colleagues,

We are pleased to invite you to contribute to this Special Issue, titled: Machine Learning Algorithms and Their Applications in Bioinformatics. In recent years, machine learning has become a transformative force in the field of bioinformatics, driving advances in genomics, proteomics, systems biology, drug discovery, and personalized medicine. With the exponential growth of biological data, the demand for intelligent algorithms capable of extracting meaningful patterns and insights is more pressing than ever. This interdisciplinary research area lies at the intersection of computational science, biology, and medicine, and continues to produce novel solutions to complex biomedical challenges.

This Special Issue aims to explore the recent developments, methodologies, and applications of machine learning algorithms in bioinformatics. We seek to gather high-quality research contributions that demonstrate innovative uses of supervised, unsupervised, semi-supervised, and deep learning techniques in analyzing and interpreting biological data. The topics addressed in this Special Issue are well-aligned with the scope of Mathematics, as they contribute to advancements in computational tools and theoretical frameworks for biology and medicine.

Both original research articles and reviews are welcome in this Special Issue. The areas of research may include (but are not limited to) the following:

• Machine learning for genomic and transcriptomic data analysis;
• Deep learning approaches for predicting protein structure and function;
• The discovery of AI-based biomarkers and diagnostic tools;
• Predictive modeling for drug–target interactions and drug repurposing;
• Machine learning for systems biology and metabolic network analysis;
• Feature selection and dimensionality reduction in high-dimensional omics data;
• The interpretability and explainability of machine learning models for biomedical applications;
• Automated Machine Learning (AutoML) frameworks for bioinformatics workflows;
• ML techniques for small- or limited-sample biological datasets;
• Multi-omics data integration using advanced machine learning techniques;
• Personalized and precision medicine supported by ML-driven models;
• The benchmarking, validation, and reproducibility of ML approaches in bioinformatics.

We look forward to receiving your contributions.

Dr. Alireza Khanteymoori
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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.

• machine learning in bioinformatics
• deep learning in biomedical research
• genomic data analysis
• protein structure prediction
• biomarker discovery
• drug-target interaction prediction
• systems biology modeling
• omics data integration
• feature selection in high-dimensional data
• explainable artificial intelligence (XAI)
• automated machine learning (AutoML)
• small sample learning in biology
• semi-supervised learning in bioinformatics
• unsupervised learning for biological insights
• multi-omics analysis