Artificial Intelligence Research for Complex Biological Systems (2nd Edition)

A Special Issue of Biology (ISSN 2079-7737) belonging to the section "Bioinformatics".

Deadline for manuscript submissions: 30 June 2027 | Viewed by 423

Editors

Department of Biology and Biomedical Sciences, Rowan University, Glassboro, NJ 08028, USA
Interests: single-cell omics data analysis; deep learning; mathematical modeling; cancer epigenetics; neuroscience
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Guest Editor
Scojen Institute of Synthetic Biology, Reichman University, Hertsliya 4610101, Israel
Interests: chimeric RNAs; fusion proteins and liquid biopsy in complex diseases; machine learning and genomics algorithms
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This collection is the second edition of the previous issue, "Artificial Intelligence Research for Complex Biological Systems". Advancements in intelligent computing technology have significantly propelled cutting-edge research within the realms of bioinformatics, computational systems biology and related fields. Intelligent computing methodologies have assumed a progressively pivotal role in both biological and medical research to investigate complex biological systems. Notably, intelligent computational approaches have demonstrated their efficacy in analyzing single-cell and bulk omics data, elucidating dynamic mechanisms pertinent to cancer and neuroscience and proficiently modeling and optimizing intricate biological systems. Single-cell omics data, characterized by their complex format, voluminous nature, high data dimensionality, suboptimal data quality and pronounced levels of noise, have become a focal point in contemporary computational biology research, underscoring the importance of employing intelligent computing techniques for their analysis and interpretation. The anticipated emergence of generative artificial intelligence offers promise in establishing foundational computational frameworks, thereby fostering systematic advancements in biological and biomedical research. Consequently, we are pleased to announce an upcoming Special Issue titled “Artificial Intelligence Research for Complex Biological Systems (2nd Edition)” in the journal Biology [MDPI]. The overarching objective of this Special Issue is to showcase the latest breakthroughs in the fields of bioinformatics, computational systems biology and modern genomics, and we welcome submissions of technical papers focusing on data-driven studies leveraging intelligent computing technologies.

Dr. Yong Chen
Dr. Milana Frenkel-Morgenstern
Guest Editors

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Keywords

  • deep learning
  • generative artificial intelligence
  • bioinformatics
  • complex biological systems
  • cancer
  • neuroscience
  • next-generation sequencing
  • single-cell and bulk omics
  • genomics

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Published Papers (1 paper)

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Review

18 pages, 1021 KB  
Review
Biological Foundation Models for Complex Disease Research and Clinical Translation
by Tiana Noll-Walker and Yong Chen
Biology 2026, 15(17), 1527; https://doi.org/10.3390/biology15171527 - 3 Sep 2026
Viewed by 205
Abstract
Complex diseases, including cancer, rare genetic disorders, neurodevelopmental and psychiatric conditions, and neurodegenerative diseases, arise from interactions among genetic variation, gene regulation, and cellular states that are difficult to capture using a single data type or biological scale. Biological foundation models address this [...] Read more.
Complex diseases, including cancer, rare genetic disorders, neurodevelopmental and psychiatric conditions, and neurodegenerative diseases, arise from interactions among genetic variation, gene regulation, and cellular states that are difficult to capture using a single data type or biological scale. Biological foundation models address this challenge by treating nucleotides and genes as tokens and learning representations that can be transferred to downstream biomedical and clinical tasks. In this review, we examine two major model classes, genomic sequence foundation models and cell foundation models, and compare their tokenization strategies, model architectures, pretraining objectives, and adaptation methods. We summarize their emerging applications in regulatory variant interpretation, disease-associated cell-state analysis, drug-response prediction, and therapeutic target discovery across complex diseases. We distinguish applications supported by experimental or retrospective validation from those that remain primarily computational or conceptual. We further discuss key challenges to clinical translation, including multimodal data integration, model interpretability, benchmarking, patient-specific prediction, and privacy protection. We highlight future opportunities to integrate biological foundation models with emerging frameworks of medical digital twins, agentic AI, and federated learning. By linking model design to translational goals, this review provides a practical framework for evaluating biological foundation models and their readiness for complex disease research and clinical use. Full article
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