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AI, ML and Bioinformatics in Molecular Mechanisms of Human Health and Disease

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Informatics".

Deadline for manuscript submissions: 30 November 2026 | Viewed by 844

Special Issue Editor

Dr. Santosh Anand

E-Mail Website
Guest Editor
Division of Biological Sciences, University of Missouri, Columbia, MO 65211, USA
Interests: bioinformatics; data science; machine learning; artificial intelligence; cancer; clinical trials; drug discovery; biomarkers
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI), machine learning (ML), and bioinformatics are revolutionizing our understanding of molecular mechanisms underlying human health and disease. These computational approaches are enabling the integration and analysis of large-scale biological data, leading to new insights into disease etiology, diagnosis, and therapeutic strategies. This Special Issue, “Artificial Intelligence, Machine Learning, and Bioinformatics in Molecular Mechanisms of Human Health and Disease,” aims to highlight cutting-edge research that leverages AI/ML and bioinformatics to uncover the molecular basis of health and disease.

We welcome original research papers, short communications, and review articles that cover topics including, but not limited to, the following:

  • Development and application of AI and machine learning algorithms in bioinformatics for disease prediction, diagnosis, and treatment;
  • Integration of multi-omics data (genomics, transcriptomics, proteomics, and metabolomics) to elucidate molecular mechanisms in health and disease;
  • Computational modelling of molecular networks and pathways relevant to human diseases;
  • Novel bioinformatics tools and databases for analyzing large-scale biological datasets;
  • Applications of AI and bioinformatics in personalized medicine and precision health;
  • Case studies demonstrating the impact of AI and bioinformatics on understanding complex diseases;
  • Study of molecular processes linked to healthy aging, resilience, and adaptation using AI/ML and bioinformatics;
  • Discovery of protective genetic factors and health-promoting molecular signatures using population genomics and AI/ML.

We look forward to receiving your contributions and advancing the interdisciplinary field of AI/ML and bioinformatics in molecular sciences.

Dr. Santosh Anand
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. International Journal of Molecular Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. There is an Article Processing Charge (APC) for publication in this open access journal. For details about the APC please see here. 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
  • machine learning
  • bioinformatics
  • molecular mechanisms
  • human health
  • human disease
  • multi-omics data
  • molecular pathways
  • personalized medicine
  • disease biomarkers

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Published Papers (3 papers)

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Research

23 pages, 34240 KB  
Article
miRNA-Mediated Signaling Networks in Non-Small Cell Lung Cancer: Linking Tumor Progression to Sarcopenia
by Swati Goswami, Pooja Gulhane and Shailza Singh
Int. J. Mol. Sci. 2026, 27(11), 4703; https://doi.org/10.3390/ijms27114703 - 23 May 2026
Abstract
Non-small cell lung cancer (NSCLC) remains a major cause of cancer-related mortality, with poor survival outcomes despite advances in surgery, chemotherapy, targeted therapy, and immunotherapy. The tumor microenvironment (TME) plays a central role in sustaining tumor growth, immune evasion, and systemic metabolic dysfunction. [...] Read more.
Non-small cell lung cancer (NSCLC) remains a major cause of cancer-related mortality, with poor survival outcomes despite advances in surgery, chemotherapy, targeted therapy, and immunotherapy. The tumor microenvironment (TME) plays a central role in sustaining tumor growth, immune evasion, and systemic metabolic dysfunction. In this study, we performed an integrative analysis of differentially expressed microRNAs (miRNAs) to uncover their contributions to dysregulated signaling networks in NSCLC. hsa-miR-486-5p was identified as a prominent differentially expressed candidate miRNA. Using mathematical modeling and regression-based reduction, we identified Forkhead Box O1 (FOXO1) and Unc-51 like Autophagy Activating Kinase 2 (ULK2) as critical regulatory nodes that integrate oncogenic signaling with cellular homeostasis. Aberrant expression of hsa-miR-486-5p was found to modulate pathways including PI3K/AKT/mTOR, NF-κB, and JAK-STAT3, thereby promoting tumor progression and secretion of inflammatory cytokines. These cytokines, viz., IL-6, TNF-α, and IL-1β, activate muscle-specific protein degradation pathways through E3 ubiquitin ligases TRIM63 and FBXO32, linking NSCLC progression to cancer-associated sarcopenia. Quasipotential landscape analysis further revealed dynamic phenotypic transitions between stable and unstable states, highlighting the adaptability of tumor–host interactions. Collectively, our findings demonstrate that miRNA-mediated regulatory networks not only drive NSCLC progression and inflammation but also contribute to systemic muscle wasting. These insights emphasize the need for novel therapeutic strategies, including RNA-based interventions, to overcome resistance, improve survival, and address the metabolic complications associated with NSCLC. Full article
(This article belongs to the Special Issue AI, ML and Bioinformatics in Molecular Mechanisms of Human Health and Disease)
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26 pages, 11166 KB  
Article
Integrative Transcriptomic Analysis Identifies Shared Immune–Fibrotic Transcriptional Programs Across Crohn’s Disease and Idiopathic Pulmonary Fibrosis
by Renwei Luo, Qiong Zhang, Qinglu Fan, Qingyun Chen, Zhihao Nie, Lingxuan Dan, Fengling Luo, Yige Cao and Songping Xie
Int. J. Mol. Sci. 2026, 27(10), 4428; https://doi.org/10.3390/ijms27104428 - 15 May 2026
Viewed by 118
Abstract
Idiopathic pulmonary fibrosis (IPF) and Crohn’s disease (CD) share overlapping immune and fibrotic processes, yet their convergent molecular mechanisms remain poorly defined. Here, we performed an integrative transcriptomic analysis of nine public datasets to identify shared transcriptional signatures across IPF and CD. The [...] Read more.
Idiopathic pulmonary fibrosis (IPF) and Crohn’s disease (CD) share overlapping immune and fibrotic processes, yet their convergent molecular mechanisms remain poorly defined. Here, we performed an integrative transcriptomic analysis of nine public datasets to identify shared transcriptional signatures across IPF and CD. The main discovery and validation analyses were based on bulk transcriptomic datasets and combined differential expression profiling, weighted gene co-expression network analysis, and machine-learning–based feature prioritization. We identified 28 shared disease-associated module genes, from which three core genes—ZNF395, EEF2K, and BAHD1—were prioritized based on reproducibility and biological consistency. Functional enrichment analysis revealed their involvement in immune regulation, protein homeostasis, and stress-response pathways. Immune deconvolution and supportive single-cell RNA-sequencing further suggested associations between these genes and T-cell and myeloid cell populations, suggesting coordinated immune-fibrotic regulation. Experimental validation in a repetitive bleomycin challenge model and TGF-β1-stimulated fibroblasts showed consistent downregulation of these genes during fibrotic remodeling, supporting their association with fibrosis-related transcriptional states. Collectively, our study identifies conserved immune–fibrotic transcriptional programs shared across intestinal inflammation and pulmonary fibrosis, providing a hypothesis-generating molecular framework for understanding extraintestinal pulmonary involvement in Crohn’s disease and prioritizing candidate genes for future mechanistic investigation. Full article
(This article belongs to the Special Issue AI, ML and Bioinformatics in Molecular Mechanisms of Human Health and Disease)
23 pages, 9416 KB  
Article
Integrated Single-Cell and Bulk RNA Sequencing Identifies Macrophage Heterogeneity and Mitophagy-Related Biomarkers in Idiopathic Pulmonary Fibrosis
by Chen Shang and Gao Huang
Int. J. Mol. Sci. 2026, 27(10), 4201; https://doi.org/10.3390/ijms27104201 - 8 May 2026
Viewed by 248
Abstract
Mitophagy clears damaged mitochondria and maintains normal macrophage function. Clarifying the associations between idiopathic pulmonary fibrosis (IPF), macrophages, and mitophagy is crucial for early diagnosis and clinical management. Core macrophage subsets were identified as M2 macrophages via single-cell RNA sequencing and immune infiltration [...] Read more.
Mitophagy clears damaged mitochondria and maintains normal macrophage function. Clarifying the associations between idiopathic pulmonary fibrosis (IPF), macrophages, and mitophagy is crucial for early diagnosis and clinical management. Core macrophage subsets were identified as M2 macrophages via single-cell RNA sequencing and immune infiltration analysis. Differentially expressed genes related to this subset were obtained. Integrated differential expression analysis, weighted gene co-expression network analysis, machine learning, and expression verification were applied to screen biomarkers. CD163 and SPP1 were identified through biomarker screening, both showing significantly increased expression in IPF. Functional enrichment showed that these biomarkers are mainly involved in cell cycle checkpoints and ciliopathies. Immune microenvironment analysis identified 16 immune cell types with significant differences between IPF and control groups, among which T helper 2 cells were strongly positively correlated with CD163. A total of nine drugs were found to be associated with CD163 and SPP1. The expression of these biomarkers changed dynamically during M2 macrophage differentiation. This study integrates single-cell and bulk transcriptomics analysis to reveal the critical roles of CD163 and SPP1 in the IPF macrophage–mitochondrial autophagy axis, a novel framework for understanding the macrophage–mitophagy axis in IPF pathogenesis. Full article
(This article belongs to the Special Issue AI, ML and Bioinformatics in Molecular Mechanisms of Human Health and Disease)
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