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Biomedicines
Special Issues
Bioinformatics Analysis of RNA for Human Health and Disease
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Bioinformatics Analysis of RNA for Human Health and Disease

A special issue of Biomedicines (ISSN 2227-9059). This special issue belongs to the section "Molecular Genetics and Genetic Diseases".

Deadline for manuscript submissions: 30 November 2025 | Viewed by 2129

Special Issue Editor

Dr. Jihwan Ha

E-Mail Website
Guest Editor
Major of Big Data Convergence, Division of Data Information Science, Pukyoung National University, Busan 48513, Republic of Korea
Interests: bioinformatics; computational biology; artificial intelligence; machine learning; deep learning; data mining; recommender system

Special Issue Information

Dear Colleagues,

The rapid advancement of high-throughput technologies has enabled the generation of vast amounts of omics data, including transcriptomics, providing unprecedented opportunities to explore the molecular mechanisms underlying human disease. This wealth of data has catalyzed significant progress in understanding RNA biology and its diverse roles in cellular processes and disease pathogenesis. RNAs, including miRNAs, lncRNAs, circRNAs, and other types, have emerged as critical players in gene regulation, cellular communication, and disease progression. Over the past decade, substantial efforts have been made to identify RNA molecules as biomarkers and therapeutic targets. However, challenges such as the limited availability of experimentally validated RNA interactions and functional annotations persist. Addressing these challenges requires a combination of robust computational approaches and innovative experimental methodologies to generate and validate insights. This Special Issue aims to highlight advancements in RNA research by welcoming contributions from diverse approaches, including computational modeling, experimental biology, and interdisciplinary studies. Topics of interest include, but are not limited to, the following:

  • The identification and validation of RNA biomarkers across all RNA types (e.g., miRNAs, lncRNAs, circRNAs, tRNAs, and others).
  • Experimental techniques for studying RNA–protein and RNA–disease interactions.
  • Multi-omics approaches to investigate RNA functions and regulatory networks.
  • Single-cell transcriptomics and its application in RNA research.
  • The structural and functional characterization of RNAs using experimental and computational methods.
  • The development of therapeutic strategies targeting RNA.
  • The integration of computational predictions with experimental validation for RNA-related discoveries.

We encourage submissions that span the full spectrum of RNA research, from experimental studies unveiling novel biological mechanisms to data-driven approaches generating hypotheses for further investigation. This Special Issue seeks to foster collaboration and innovation in the rapidly evolving field of RNA biology, ultimately contributing to the understanding and treatment of human disease. We look forward to your contributions which, we believe, will shape the future of this exciting area of research.

Dr. Jihwan Ha
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. Biomedicines 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

  • bioinformatics
  • computational biology
  • machine learning
  • multi-omics data analysis
  • biomarker detection
  • biomedical AI convergence
  • medical and public health informatics
  • RNA sequencing (RNA-Seq)
  • functional genomics
  • RNA-protein interactions
  • experimental validation of biomarkers
  • RNA structural biology
  • transcriptome-wide association studies (TWAS)
  • gene expression profiling
  • RNA modifications (e.g., m6A, pseudouridylation)
  • in vitro and in vivo RNA functional studies
  • single-cell RNA analysis
  • RNA therapeutics development

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Published Papers (2 papers)

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Research

20 pages, 3030 KiB  
Article
DeepWalk-Based Graph Embeddings for miRNA–Disease Association Prediction Using Deep Neural Network
by Jihwan Ha
Biomedicines 2025, 13(3), 536; https://doi.org/10.3390/biomedicines13030536 - 20 Feb 2025
Viewed by 563
Abstract
Background: In recent years, micro ribonucleic acids (miRNAs) have been recognized as key regulators in numerous biological processes, particularly in the development and progression of diseases. As a result, extensive research has focused on uncovering the critical involvement of miRNAs in disease mechanisms [...] Read more.
Background: In recent years, micro ribonucleic acids (miRNAs) have been recognized as key regulators in numerous biological processes, particularly in the development and progression of diseases. As a result, extensive research has focused on uncovering the critical involvement of miRNAs in disease mechanisms to better comprehend the underlying causes of human diseases. Despite these efforts, relying solely on biological experiments to identify miRNA-disease associations is both time-consuming and costly, making it an impractical approach for large-scale studies. Methods: In this paper, we propose a novel DeepWalk-based graph embedding method for predicting miRNA–disease association (DWMDA). Using DeepWalk, we extracted meaningful low-dimensional vectors from the miRNA and disease networks. Then, we applied a deep neural network to identify miRNA–disease associations using the low-dimensional vectors of miRNAs and diseases extracted via DeepWalk. Results: An ablation study was conducted to assess the proposed graph embedding modules. Furthermore, DWMDA demonstrates exceptional performance in two major cancer case studies (breast and lung), with results based on statistically robust measures, further emphasizing its reliability as a method for identifying associations between miRNAs and diseases. Conclusions: We expect that our model will not only facilitate the accurate prediction of disease-associated miRNAs but also serve as a generalizable framework for exploring interactions among various biological entities. Full article
(This article belongs to the Special Issue Bioinformatics Analysis of RNA for Human Health and Disease)
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17 pages, 2756 KiB  
Article
Graph Convolutional Network with Neural Collaborative Filtering for Predicting miRNA-Disease Association
by Jihwan Ha
Biomedicines 2025, 13(1), 136; https://doi.org/10.3390/biomedicines13010136 - 8 Jan 2025
Cited by 2 | Viewed by 814
Abstract
Background: Over the past few decades, micro ribonucleic acids (miRNAs) have been shown to play significant roles in various biological processes, including disease incidence. Therefore, much effort has been devoted to discovering the pivotal roles of miRNAs in disease incidence to understand the [...] Read more.
Background: Over the past few decades, micro ribonucleic acids (miRNAs) have been shown to play significant roles in various biological processes, including disease incidence. Therefore, much effort has been devoted to discovering the pivotal roles of miRNAs in disease incidence to understand the underlying pathogenesis of human diseases. However, identifying miRNA–disease associations using biological experiments is inefficient in terms of cost and time. Methods: Here, we discuss a novel machine-learning model that effectively predicts disease-related miRNAs using a graph convolutional neural network with neural collaborative filtering (GCNCF). By applying the graph convolutional neural network, we could effectively capture important miRNAs and disease feature vectors present in the network while preserving the network structure. By exploiting neural collaborative filtering, miRNAs and disease feature vectors were effectively learned through matrix factorization and deep learning, and disease-related miRNAs were identified. Results: Extensive experimental results based on area under the curve (AUC) scores (0.9216 and 0.9018) demonstrated the superiority of our model over previous models. Conclusions: We anticipate that our model could not only serve as an effective tool for predicting disease-related miRNAs but could be employed as a universal computational framework for inferring relationships across biological entities. Full article
(This article belongs to the Special Issue Bioinformatics Analysis of RNA for Human Health and Disease)
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