Introduction

The convergence of multi-omics technologies, artificial intelligence (AI), and systems biology is revolutionizing biomedical research and clinical practice. This synergy enables unprecedented capabilities to decode complex disease mechanisms, predict therapeutic responses, and personalize healthcare. However, the exponential growth of heterogeneous data—spanning genomics, proteomics, clinical records, and real-world evidence—demands advanced computational frameworks for integration, analysis, and translation.

This Collection seeks to showcase cutting-edge methodologies and applications that bridge the gap between massive biological data and actionable clinical insights, driving the next wave of precision medicine.

Despite rapid advancements, critical barriers persist:

(1) Technical fragmentation—multi-modal data (e.g., single-cell RNA-seq + medical imaging) lack standardized integration pipelines;

(2) Clinical translatability—AI models often operate as "black boxes" with limited interpretability for medical decision-making;

(3) Scalability bottlenecks—processing petabyte-scale data lakes requires novel distributed computing strategies;

(4) Biological validation—systems-level predictions (e.g., cell fate trajectories) need experimental grounding.

Additionally, ethical frameworks for deploying real-time health data streams remain underdeveloped. Addressing these challenges necessitates interdisciplinary collaboration across computational biology, clinical informatics, and AI engineering.

This Special Collection aims to curate high-impact research that pioneers intelligent, scalable, and clinically deployable solutions for precision health. We invite contributions that:

  • Develop novel algorithms for multi-scale data harmonization and knowledge extraction;
  • Enhance AI transparency and robustness in medical contexts;
  • Translate computational discoveries into validated diagnostic/therapeutic tools;
  • Establish ethical guidelines for big-data-driven healthcare.

By uniting foundational research and translational applications, this collection will accelerate the transition from data-rich to insight-rich biomedicine.


Topics for this call for papers include but are not restricted to:

  • Cross-omics integration frameworks for joint analysis of genomic, transcriptomic, and metabolomic data in cancer heterogeneity.
  • Spatial multi-omics computational tools resolving tissue microenvironment architecture in chronic diseases.
  • Single-cell multi-modal analytics linking epigenetics, proteomics, and cell-cell communication networks.
  • Deep learning architectures for predicting RNA tertiary structures and functional interactions.
  • Federated learning systems enabling privacy-preserving analysis of distributed clinical data lakes.
  • Interpretable neural networks for explainable diagnosis of rare diseases from electronic health records.
  • Reinforcement learning pipelines optimizing dynamic treatment regimens in critical care.
  • Microbiome-metabolome causal modeling identifying drivers of host metabolic disorders.
  • Graph neural networks (GNNs) mapping drug-target-disease tripartite networks for repurposing.
  • Digital twin frameworks simulating organ-level pathophysiology for intervention testing.
  • Real-time stream processing engines for early warning of infectious disease outbreaks.
  • Multiscale mechanistic models predicting cell fate decisions in regenerative medicine.
  • Bayesian federated meta-learning generalizing clinical risk models across diverse populations.
  • Adversarial robustness validation of AI-based diagnostic tools against dataset shift.
  • Blockchain-secured platforms for auditable sharing of multi-institutional health data.

Collection Editors

Peng Luo ([email protected]), Department of Oncology, Zhujiang Hospital, Southern Medical University, China

Kai Miao ([email protected]), Cancer Centre and Institute of Translational Medicine, University of Macau, China

Guangsheng Pei ([email protected]), University of Texas MD Anderson Cancer Center, Houston, TX, USA

Ulf D Kahlert ([email protected]), Otto-von Guericke University Magdeburg, Germany

Lin Zhang ([email protected]), The School of Public Health and Preventive Medicine, Monash University, Australia

András Hajdu ([email protected]), Department of Data Visualization, Faculty of Informatics, University of Debrecen, Hungary


Participating Journals


Submission Guidelines

Researchers from diverse scientific backgrounds (including computational biology, AI/ML, clinical informatics, and systems medicine) are encouraged to submit original research and reviews (additional article types per journal guidelines). All submissions will undergo rigorous peer review to ensure the highest quality and scientific integrity. This collection specifically seeks interdisciplinary contributions that bridge computational innovation and clinical translation, accelerating the development of actionable precision health solutions. We anticipate this collection will catalyze cross-domain collaboration and look forward to your contributions advancing intelligent precision medicine.

If you wish to submit an article to this collection, please select the journal that best aligns with your research focus from the above listed options. When submitting your manuscript, please answer the question: "Is this submission for a special issue?" by selecting the collection title “Intelligent Integration for Precision Health: Multi-Omics, AI, and Systems Biology Approaches” from the drop-down list. This will help us accurately identify and consider your manuscript for the collection.


Author Services

The website provides easy-to-access publishing resources and training tools for authors, reviewers, and editors. Please refer to the Author Guidelines of the selected journal to prepare your manuscript.


Publication

Accepted papers will initially be published in a regular issue of the journal. Once the collection is complete, all articles will be complied into a virtual collection to enhance visibility and accessibility. The collection will be actively promoted through Wiley's social media channels, ensuring broad dissemination of the research.


Open Access and Transformational Agreement (TA)

Wiley offers multiple pathways for Open Access publication. Authors affiliated with institutions participating in Wiley’s Transformational Agreements (TAs) may qualify for full or partial coverage of Open Access fees. Please find more details and check eligibility here: Transformational Agreements.

Wiley is also a partner of Research 4 Life which automatically provides full and partial waivers of article processing charges for manuscripts based on the corresponding author's listed affiliation covered. Starting in 2025, authors affiliated with institutions in 33 Latin America countries (listed here) are eligible for discount on APCs.


Contact details

If you any queries, please contact the respective journal’s Editorial Office.

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