AI and Data Driven Modeling in Computational Biology and Bioinformatics
A special issue of Technologies (ISSN 2227-7080). This special issue belongs to the section "Information and Communication Technologies".
Deadline for manuscript submissions: 31 December 2026 | Viewed by 278
Editor
Interests: artificial intelligence in life sciences; graph representation learning; biomedical knowledge graphs; drug repositioning; machine learning for omics data; computational drug discovery; protein and molecular modeling
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Recent advances in artificial intelligence (AI) are rapidly reshaping the landscape of computational biology and bioinformatics, particularly in the era of big biomedical data. The increasing availability of heterogeneous and large-scale datasets—including multi-omics profiles, clinical records, and biomedical literature—has created unprecedented opportunities for data-driven discovery. Among emerging methodologies, graph representation learning and biomedical knowledge graphs have demonstrated unique advantages in modeling complex biological systems, enabling the integration of multi-source data and uncovering hidden relationships across genes, diseases, and drugs.
In parallel, AI-driven approaches are accelerating translational applications such as drug repositioning, target identification, and precision medicine. By leveraging graph-based learning, deep neural networks, and integrative modeling strategies, researchers can systematically explore biological mechanisms and improve decision-making in drug development pipelines. However, challenges remain in model interpretability, data heterogeneity, and real-world deployment, highlighting the need for continued methodological innovation and interdisciplinary collaboration.
This Special Issue aims to present and disseminate cutting-edge advances in AI and data-driven modeling in computational biology and bioinformatics, with a particular emphasis on graph-based methods and knowledge-driven approaches. We welcome contributions that develop novel computational frameworks, integrate multi-modal biological data, or demonstrate impactful applications in biomedical research and healthcare.
Topics of interest for publication include, but are not limited to, the following:
- Graph representation learning in biology and medicine;
- Biomedical knowledge graphs and knowledge-driven modeling;
- AI-based drug repositioning and drug discovery;
- Multi-omics data integration and network biology;
- Machine learning and deep learning for bioinformatics;
- Protein structure prediction and molecular modeling;
- Single-cell and spatial omics analysis;
- Explainable and trustworthy AI in biomedical applications;
- Clinical data mining and precision medicine;
- AI-enabled translational bioinformatics.
Dr. Bo-Wei Zhao
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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Technologies 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 1800 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
- artificial intelligence
- graph representation learning
- biomedical knowledge graph
- drug repositioning
- computational biology
- bioinformatics
- multi-omics integration
- machine learning
- precision medicine
- network biology
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