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Applications of Machine Learning in Bioinformatics and Biomedicine
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Applications of Machine Learning in Bioinformatics and Biomedicine

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: 31 July 2025 | Viewed by 1976

Special Issue Editor

Dr. Fuyi Li

E-Mail Website
Guest Editor
Artificial Intelligence for Biological Innovation (ABI Lab), South Australian Immunogenomics Cancer Institute (SAiGENCI), The University of Adelaide, Adelaide, Australia
Interests: machine learning; data mining; bioinformatics; biomacromolecular covalent modifications; host-pathology interaction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue seeks to explore the innovative application of machine learning (ML) techniques in the fields of bioinformatics and biomedicine. It aims to gather a collection of high-quality research articles that demonstrate how ML can address complex biological and medical challenges, contributing to advancements in disease understanding, diagnosis, treatment, and prevention.

The scope of this Special Issue includes, but is not limited to:

  • Development and application of novel ML algorithms to analyze genomic, proteomic, metabolomic, and clinical data.
  • Integration of multi-omics data to discover biomarkers and therapeutic targets using ML approaches.
  • Application of deep learning in image analysis for biomedical imaging and cellular morphology.
  • ML in personalized medicine, including predictive models for patient diagnosis and prognosis based on individual genomic and clinical profiles.
  • Advances in drug discovery and design utilizing ML to predict molecular behavior and drug interactions.

We encourage submissions that not only showcase technological advancements but also significantly impact the theoretical understanding of the application area. Submissions should clearly articulate the biological and medical relevance of the ML techniques employed, highlighting their potential to lead to actionable insights and clinical applications.

Dr. Fuyi Li
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 100 words) can be sent to the Editorial Office for announcement on this website.

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

  • bioinformatics
  • computational biology
  • machine learning
  • deep learning
  • AI for biology

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

Published Papers (3 papers)

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Research

31 pages, 8559 KiB  
Article
GPX1 and RCN1 as New Endoplasmic Reticulum Stress-Related Biomarkers in Multiple Sclerosis Brain Tissue and Their Involvement in the APP-CD74 Pathway: An Integrated Study Combining Machine Learning and Multi-Omics
by Zhixin Qiao, Yanping Wang, Xiaoru Ma, Xiyu Zhang, Junfeng Wu, Anqi Li, Chao Wang, Xin Xiu, Sifan Zhang, Xiujuan Lang, Xijun Liu, Bo Sun, Hulun Li and Yumei Liu
Int. J. Mol. Sci. 2025, 26(13), 6286; https://doi.org/10.3390/ijms26136286 - 29 Jun 2025
Viewed by 532
Abstract
This study identified 13 endoplasmic reticulum stress (ERS)-related biomarkers associated with multiple sclerosis (MS) through integrated bioinformatics analysis (including weighted gene co-expression network analysis and machine learning algorithms) and single-cell sequencing, combined with validation in an experimental autoimmune encephalomyelitis (EAE) mouse model. Among [...] Read more.
This study identified 13 endoplasmic reticulum stress (ERS)-related biomarkers associated with multiple sclerosis (MS) through integrated bioinformatics analysis (including weighted gene co-expression network analysis and machine learning algorithms) and single-cell sequencing, combined with validation in an experimental autoimmune encephalomyelitis (EAE) mouse model. Among them, GPX1, RCN1, and UBE2D3 exhibited high diagnostic value (AUC > 0.7, p < 0.05), and the diagnostic potential of GPX1 and RCN1 was confirmed in the animal model. The study found that memory B cells, plasma cells, neutrophils, and M1 macrophages were significantly increased in MS patients, while naive B cells and activated NK cells decreased. Consensus clustering based on key ERS-related genes divided MS patients into two subtypes. Single-cell sequencing showed that microglia and pericytes were the cell types with the highest expression of key ERS-related genes, and the APP-CD74 pathway was enhanced in the brain tissue of MS patients. Mendelian randomization analysis suggested that GPX1 plays a protective role in MS. These findings reveal the mechanisms of ERS-related biomarkers in MS and provide potential targets for diagnosis and treatment. Full article
(This article belongs to the Special Issue Applications of Machine Learning in Bioinformatics and Biomedicine)
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17 pages, 1584 KiB  
Article
Evaluating Genetic Regulators of MicroRNAs Using Machine Learning Models
by Mert Cihan, Uchenna Alex Anyaegbunam, Steffen Albrecht, Miguel A. Andrade-Navarro and Maximilian Sprang
Int. J. Mol. Sci. 2025, 26(12), 5757; https://doi.org/10.3390/ijms26125757 - 16 Jun 2025
Viewed by 483
Abstract
This study explores the genetic regulators of microRNAs (miRNAs) using a set of machine learning models to predict miRNA expression levels from gene expression data. Employing machine learning, we accurately predicted the expression of 353 human miRNAs (R2 > 0.5), revealing robust [...] Read more.
This study explores the genetic regulators of microRNAs (miRNAs) using a set of machine learning models to predict miRNA expression levels from gene expression data. Employing machine learning, we accurately predicted the expression of 353 human miRNAs (R2 > 0.5), revealing robust miRNA–gene regulatory relationships. By analyzing the coefficients of these predictive models, we identified genetic regulators for each miRNA and highlighted the multifactorial nature of miRNA regulation. Further network analysis uncovered that miRNAs with higher predictive accuracy are more densely connected to their top predictive genes, reflecting strong regulatory control within miRNA–gene networks. To refine these insights, we filtered the miRNA–gene interaction networks to identify miRNAs specifically associated with enriched pathways, such as synaptic function and cardiovascular processes. From this pathway-centric analysis, we present a curated list of miRNAs and their genetic regulators, pinpointing their activity within distinct biological contexts. Additionally, our study provides a comprehensive set of metrics and coefficients for the genes most predictive of miRNA expression, along with a filtered subnetwork of miRNAs linked to specific pathways and phenotypes. By integrating miRNA expression predictors with network analysis and pathway enrichment, this work advances our understanding of miRNA regulatory mechanisms and their roles across distinct biological systems. Our approach enables researchers to train custom models using TCGA data and predict miRNA expression from gene expression inputs. Full article
(This article belongs to the Special Issue Applications of Machine Learning in Bioinformatics and Biomedicine)
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16 pages, 4091 KiB  
Article
TFProtBert: Detection of Transcription Factors Binding to Methylated DNA Using ProtBert Latent Space Representation
by Saima Gaffar, Kil To Chong and Hilal Tayara
Int. J. Mol. Sci. 2025, 26(9), 4234; https://doi.org/10.3390/ijms26094234 - 29 Apr 2025
Viewed by 444
Abstract
Transcription factors (TFs) are fundamental regulators of gene expression and perform diverse functions in cellular processes. The management of 3-dimensional (3D) genome conformation and gene expression relies primarily on TFs. TFs are crucial regulators of gene expression, performing various roles in biological processes. [...] Read more.
Transcription factors (TFs) are fundamental regulators of gene expression and perform diverse functions in cellular processes. The management of 3-dimensional (3D) genome conformation and gene expression relies primarily on TFs. TFs are crucial regulators of gene expression, performing various roles in biological processes. They attract transcriptional machinery to the enhancers or promoters of specific genes, thereby activating or inhibiting transcription. Identifying these TFs is a significant step towards understanding cellular gene expression mechanisms. Due to the time-consuming and labor-intensive nature of experimental methods, the development of computational models is essential. In this work, we introduced a two-layer prediction framework based on a support vector machine (SVM) using the latent space representation of a protein language model, ProtBert. The first layer of the method reliably predicts and identifies transcription factors (TFs), and in the second layer, the proposed method predicts and identifies transcription factors that prefer binding to methylated deoxyribonucleic acid (TFPMs). In addition, we also tested the proposed method on an imbalanced database. In detecting TFs and TFPMs, the proposed model consistently outperformed state-of-the-art approaches, as demonstrated by performance comparisons via empirical cross-validation analysis and independent tests. Full article
(This article belongs to the Special Issue Applications of Machine Learning in Bioinformatics and Biomedicine)
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