AI and Network Science for Biological Systems and Human Health

A special issue of Computers (ISSN 2073-431X). This special issue belongs to the section "AI-Driven Innovations".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 1595

Editor

Faculty of Computer Science and Technology, Algoma University, Brampton, ON, Canada
Interests: deep learning; bioinformatics; cancer genomics; clinical data analytics

Special Issue Information

Dear Colleagues,

We are pleased to invite submissions to the Special Issue "AI and Network Science for Biological Systems and Human Health".

The convergence of artificial intelligence and network science is revolutionizing our understanding of complex biological systems and their implications for human health. Biological systems and human health are governed by complex networks spanning molecular interactions, cellular communication, neural circuitry, and population-level disease dynamics. By integrating AI-driven analytics with the principles of network science, researchers can now uncover hidden patterns, predict emergent behaviors, and design targeted interventions with unprecedented precision. This synergy holds transformative potential for personalized medicine, drug discovery, infectious disease modeling, and the decoding of multiscale biological organization.

This Special Issue aims to highlight cutting-edge research that harnesses AI and network science to address fundamental challenges in biology and healthcare. We seek contributions that advance theoretical frameworks, develop novel computational methods, or deliver actionable clinical or biological insights. Relevant topics include, but are not limited to, modeling of biological networks (e.g., gene regulatory, protein–protein interaction, neural, or ecological networks), AI-based prediction of network dynamics, graph-based machine learning, multi-omics data integration, network medicine, patient stratification, biomarker discovery, treatment response prediction, and infectious disease modeling. We particularly encourage submissions that address important challenges in biomedical data analysis, such as heterogeneity, sparsity, scalability, robustness, interpretability, and clinical or biological validation. Contributions that combine methodological novelty with strong biological insight or translational relevance are especially welcome.

We invite original research articles, comprehensive reviews, and application-driven case studies that present significant advances in using AI and network science to deepen our understanding of biological systems and contribute to more precise, predictive, and personalized approaches to human health.

Selected Topics (not limited to the following):

  • Graph neural networks and topological data analysis for biological networks;
  • AI-driven reconstruction and inference of gene regulatory, signaling, and metabolic networks;
  • Network medicine, including disease module identification, drug repurposing, and comorbidity analysis;
  • Multi-modal data integration using genomics, proteomics, imaging, and clinical records;
  • AI for patient stratification, biomarker discover, and treatment response prediction;
  • Spreading dynamics and intervention strategies for infectious diseases;
  • Neural circuit analysis and brain network modeling for neurological disorders;
  • Explainable and interpretable AI for network biology and biomedicine;
  • Scalable and robust learning on large-scale biomedical networks;
  • Applications in precision oncology, immunotherapy, aging, and population health.

Dr. Ping Luo
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. Computers 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
  • network science
  • biological networks
  • network medicine
  • graph neural networks
  • multi-omics data integration
  • gene regulatory networks
  • protein–protein interaction networks
  • neural circuit analysis
  • disease module discovery
  • drug repurposing
  • precision medicine
  • personalized healthcare
  • infectious disease modeling
  • spreading dynamics
  • topological data analysis (TDA)
  • explainable AI (XAI) in biomedicine
  • multi-modal learning in healthcare
  • patient stratification
  • biomarker discovery

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

22 pages, 8994 KB  
Article
Physics-Informed Neural Network Framework for Time-Dependent Modelling of Bacterial Quorum Sensing and Population Dynamics
by Liubov Smirnova, Andrew Gekhtin and Anna Maslovskaya
Computers 2026, 15(9), 555; https://doi.org/10.3390/computers15090555 - 24 Aug 2026
Abstract
In silico studies of microbiological systems are essential for predicting and controlling the impact of external factors on bacterial communities. Quorum sensing represents one of the key mechanisms of bacterial communication, particularly in pathogenic bacteria, realized as a cell-density-dependent regulatory process governed by [...] Read more.
In silico studies of microbiological systems are essential for predicting and controlling the impact of external factors on bacterial communities. Quorum sensing represents one of the key mechanisms of bacterial communication, particularly in pathogenic bacteria, realized as a cell-density-dependent regulatory process governed by diffusible signaling molecules. The present study proposes a Physics-Informed Neural Network (PINN)-based computational framework for a spatially independent model of bacterial quorum sensing and population dynamics. The approach solves both the forward and inverse problems for a spatially independent model formalized by a system of nonlinear ordinary differential equations. The forward problem is solved numerically by reconstructing the dynamics of three key characteristics: signaling molecule concentration, degrading enzyme concentration, and bacterial biomass density. The obtained PINN solutions are compared with numerical solutions computed using the Radau IIA implicit Runge–Kutta method. The inverse problem capability is evaluated by recovering system parameters that are difficult to measure directly in experimental settings. The framework is implemented using the DeepXDE library with a PyTorch backend, employing hard constraints for initial conditions, singularity-avoiding loss reformulations, and a multi-stage Adam–L-BFGS optimization strategy. Validation is performed on a Monod chemostat benchmark and the Pseudomonas putida IsoF quorum sensing regulatory network. The proposed PINN-based framework extends the applied mathematical toolkit for in silico studies of microbial systems, enabling accurate reconstruction of emergent population dynamics and robust inference of regulatory parameters that are inaccessible to direct experimental measurement. Full article
(This article belongs to the Special Issue AI and Network Science for Biological Systems and Human Health)
Show Figures

Graphical abstract

39 pages, 5587 KB  
Article
The Home as an Active Caregiving Partner: Scaling Zero-Interface Audiovisual Connectivity for “Aging in Place” with Dementia
by Ilyas Potamitis
Computers 2026, 15(6), 353; https://doi.org/10.3390/computers15060353 - 30 May 2026
Viewed by 1147
Abstract
Effective dementia care is often hindered by fragmented communication among patients, informal caregivers, and clinicians. To address this, we introduce an ambient assisted living (AAL) framework designed to establish a continuous, virtual, and unobtrusive connection between an elder’s home and external guardians or [...] Read more.
Effective dementia care is often hindered by fragmented communication among patients, informal caregivers, and clinicians. To address this, we introduce an ambient assisted living (AAL) framework designed to establish a continuous, virtual, and unobtrusive connection between an elder’s home and external guardians or medical staff (virtual rounds). The system enables guardians to communicate directly within the home environment, without requiring the older adult to manually accept calls or activate the connection using wearable devices, buttons, or other interfaces. The elders can activate the connection verbally. The structural core of this system relies on three novel hardware configurations designed for zero-interface operation: a remote audio announcement device, a bidirectional intercom, and a “zero-interface mirror” enabling stream-only, real-time video co-presence between patients and guardians. Crucially, the system utilizes a privacy-preserving, staged edge-AI architecture to process data. By default, it operates without long-term persistent storage, selectively transmitting abstracted audio-based behavioral metrics to a secure dashboard. For advanced dementia stages, the system employs ephemeral data retention—specifically a highly restricted, 24 h rolling audio buffer—allowing authorized guardians to verify acute events without permanently exfiltrating raw data. We evaluate this infrastructure through a 10-month longitudinal, single-home feasibility deployment, augmented with historical verified fall data to rigorously test the detection of rare acute events. The study validates the framework’s technical viability, system uptime, and privacy-first architecture in continuously tracking long-term proxy behavioral indicators under real-world conditions. Rather than asserting generalized clinical efficacy, this work demonstrates the operational feasibility of a novel, affordable, technical blueprint for dignified, remote digital care coordination. Full article
(This article belongs to the Special Issue AI and Network Science for Biological Systems and Human Health)
Show Figures

Figure 1

Back to TopTop