Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.0
3.4
Journals
Life
Special Issues
Multimodal AI for Genomic-Clinicopathologic Integration: From Preventive Screening to Precision...
share announcement

Multimodal AI for Genomic-Clinicopathologic Integration: From Preventive Screening to Precision Diagnosis

A special issue of Life (ISSN 2075-1729). This special issue belongs to the section "Biochemistry, Biophysics and Computational Biology".

Deadline for manuscript submissions: 31 July 2026 | Viewed by 2342

Special Issue Editor

Dr. Yen-Lin Chen

E-Mail Website
Guest Editor
Department of Pathology, Tri-Service General Hospital, National Defense Medical University, Taipei, Taiwan
Interests: multimodal data integration; artificial intelligence in medicine; genomic and health data analysis; digital pathology and image analysis; predictive modeling and risk stratification; explainable machine learning (XAI)

Special Issue Information

Dear Colleagues,

In the era of precision medicine, integrating diverse biomedical data sources—such as genomic profiles, structured health check-up records, and clinicopathologic imaging—is pivotal for advancing disease prevention, early detection, and individualized care. This Special Issue invites original research and reviews that leverage artificial intelligence (AI), machine learning (ML), and multimodal data fusion to address key challenges in predictive and translational medicine. We particularly welcome contributions that perform the following functions:

  1. Combine genomic data (e.g., GWAS, SNP arrays, NGS) with structured clinical or lifestyle information to identify disease risk patterns or stratify patient populations;
  2. Integrate health check-up data with AI models to develop explainable and scalable screening tools for complex diseases;
  3. Apply AI to digital pathology images (e.g., TMA, whole-slide imaging) to extract histopathological features for diagnosis, prognosis, or therapeutic response prediction;
  4. Propose innovative frameworks for variant reclassification, disease subtyping, or multimodal biomarker discovery, especially those applicable to real-world clinical data.

This interdisciplinary Special Issue aims to bridge genomics, digital pathology, and preventive healthcare, fostering novel AI-driven methodologies that support clinical decision-making and precision health strategies.

Dr. Yen-Lin Chen
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. Life 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 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.

Keywords

  • multimodal data integration
  • artificial intelligence in medicine
  • genomic and health data analysis
  • digital pathology and image analysis
  • predictive modeling and risk stratification
  • explainable machine learning (XAI)

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 (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

23 pages, 5918 KB  
Article
Machine Learning Identification of Cell-Type-Specific Molecular Signatures Distinguishing COVID-19 from Other Lower Respiratory Tract Diseases
by Yusheng Bao, Xianchao Zhou, Lei Chen, Kaiyan Feng, Wei Guo, Tao Huang and Yu-Dong Cai
Life 2026, 16(5), 771; https://doi.org/10.3390/life16050771 - 4 May 2026
Viewed by 236
Abstract
Coronavirus Disease 2019 (COVID-19) and other lower respiratory tract diseases (LRTDs), including bacterial pneumonia and acute respiratory distress syndrome, share overlapping clinical features but arise from distinct pathophysiological mechanisms. The molecular signatures that distinguish these diseases remain insufficiently characterized in African populations, where [...] Read more.
Coronavirus Disease 2019 (COVID-19) and other lower respiratory tract diseases (LRTDs), including bacterial pneumonia and acute respiratory distress syndrome, share overlapping clinical features but arise from distinct pathophysiological mechanisms. The molecular signatures that distinguish these diseases remain insufficiently characterized in African populations, where genetic background, endemic infections, and environmental exposures may substantially shape immune responses. We integrated spatially resolved single-cell transcriptomic profiles from lung autopsy specimens of 30 Malawian patients, including 10 with COVID-19, 12 with other LRTDs, and 8 non-LRTD controls. In total, 61,391 cells representing 15 cell types and 36,602 gene expression features were analyzed. Using an integrated machine learning framework that combined nine feature-ranking algorithms with incremental feature selection, we identified potential molecular signatures that could discriminate among disease states within this cohort. The optimal classification models achieved weighted F1 scores greater than 0.94, demonstrating a robust capacity to differentiate COVID-19 from other LRTDs in our dataset. Notably, the macrophage-associated state in COVID-19 was dominated by an IFN-γ response with upregulation of CD163 and HLA-DQA2, contrasting sharply with the type I/III interferon signature reported in European cohorts. In addition, we observed cell-type-specific COVID-19 signatures, including downregulation of CAV1 in AT1 cells, consistent with epithelial damage; dysregulation of SFTPC in AT2 cells, suggesting surfactant dysfunction; and upregulation of NFKBIA in neutrophils, indicating altered inflammatory regulation. Gene Ontology enrichment further revealed universal disruption of protein synthesis machinery, along with cell-type-specific alterations in immune activation, epithelial repair, and inflammatory signaling pathways. Full article
(This article belongs to the Special Issue Multimodal AI for Genomic-Clinicopathologic Integration: From Preventive Screening to Precision Diagnosis)
►▼ Show Figures

Figure 1

18 pages, 2417 KB  
Article
Advanced AI-Powered System for Comprehensive Thyroid Cancer Detection and Malignancy Risk Assessment
by Noemi Lorenzovici, Horatiu Silaghi, Eva-H. Dulf, Cornelia Braicu and Cristina Alina Silaghi
Life 2026, 16(1), 38; https://doi.org/10.3390/life16010038 - 26 Dec 2025
Viewed by 867
Abstract
The thyroid cancer incidence has been continuously rising over the last decades. Recently, intelligent cancer detection software are gaining popularity, due to their high diagnostic accuracy and subsequent direct benefits in avoiding unnecessary surgical interventions. This study introduces a novel hybrid computer-aided diagnosis [...] Read more.
The thyroid cancer incidence has been continuously rising over the last decades. Recently, intelligent cancer detection software are gaining popularity, due to their high diagnostic accuracy and subsequent direct benefits in avoiding unnecessary surgical interventions. This study introduces a novel hybrid computer-aided diagnosis (CAD) system that combines convolutional neural networks (CNNs) and molecular data analysis to achieve comprehensive and reliable thyroid cancer diagnostics. The system consists of two key modules: The first is a CNN-based model leveraging transfer learning, processes ultrasound images to classify patients as either “healthy” or “with a thyroid nodule.” In cases where a nodule is detected, the second module utilizes molecular data to predict the malignancy risk, providing a probability score for clinical decision support. Different image augmentation techniques (traditional ones as well as novels) were carried out to enhance the robustness of the system. The combination of two independent modules makes it possible to use them decoupled, while used together they provide a powerful, in-depth diagnosis of thyroid cancer. The proposed system demonstrates strong performance: the ultrasound-based CNN module achieves an accuracy of 93.65%, with a sensitivity of 100% and a specificity of 69.23%. For the gene analysis component, the model achieves a training mean squared error (MSE) of 4.24 × 10−5 and a testing MSE 6.31 × 10−3. These results underscore the system’s competitive performance with existing thyroid cancer detection CAD systems in both diagnostic performance and the depth of insights provided, supporting clinicians in making informed, reliable decisions in thyroid cancer management. Full article
(This article belongs to the Special Issue Multimodal AI for Genomic-Clinicopathologic Integration: From Preventive Screening to Precision Diagnosis)
►▼ Show Figures

Figure 1

Review

Jump to: Research

38 pages, 1831 KB  
Review
Rejection-Focused Precision Medicine in Kidney Transplantation: Biology, Biomarkers, and Artificial Intelligence
by Luis Ramalhete, Rúben Araújo, Miguel Bigotte Vieira, Emanuel Vigia, Cecília R. C. Calado and Anibal Ferreira
Life 2026, 16(4), 674; https://doi.org/10.3390/life16040674 - 15 Apr 2026
Viewed by 798
Abstract
Chronic kidney disease is rising worldwide, and kidney transplantation remains the preferred modality of kidney replacement therapy. However, long-term graft survival continues to be limited by chronic alloimmune injury, particularly antibody-mediated rejection (ABMR) and its chronic active form. This narrative review synthesizes contemporary [...] Read more.
Chronic kidney disease is rising worldwide, and kidney transplantation remains the preferred modality of kidney replacement therapy. However, long-term graft survival continues to be limited by chronic alloimmune injury, particularly antibody-mediated rejection (ABMR) and its chronic active form. This narrative review synthesizes contemporary evidence on the immunopathogenesis, epidemiology, diagnosis, and management of kidney allograft rejection, with a deliberate focus on studies from the last five years and on United States and European cohorts. We summarize current concepts of T cell–mediated rejection (TCMR), ABMR, mixed and donor-specific antibody (DSA)–negative phenotypes, and the evolution of the Banff classification, highlighting how chronic active ABMR has emerged as a leading cause of death-censored graft loss. We then critically appraise the conventional diagnostic triad of creatinine/eGFR, DSA, and biopsy and review emerging tools, including donor-derived cell-free DNA, urinary chemokines such as CXCL9 and CXCL10, additional blood- and urine-based biomarkers, and biopsy transcriptomics. We also examine how artificial intelligence and machine learning may support digital pathology, multimodal risk prediction, and data integration, while recognizing the current challenges of biological interpretability, external validation, and clinical implementation. Finally, we propose a rejection-focused precision-medicine framework and outline key research gaps, including multicenter validation, trial-ready endpoints, and governance for AI-enabled pathways. Overall, the field is moving from isolated diagnostic signals toward integrated, biologically informed, and clinically actionable approaches to rejection detection and risk stratification. Full article
(This article belongs to the Special Issue Multimodal AI for Genomic-Clinicopathologic Integration: From Preventive Screening to Precision Diagnosis)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop