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Biomolecules
Special Issues
Application of Bioinformatics in Medicine
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Application of Bioinformatics in Medicine

A special issue of Biomolecules (ISSN 2218-273X). This special issue belongs to the section "Bioinformatics and Systems Biology".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 3535

Special Issue Editor

Prof. Dr. Le Zhang

E-Mail Website
Guest Editor
Department of Computer Science, Sichuan University, Chengdu, China
Interests: bioinformatics; numerical analysis; high-performance computing and data mining
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Since bioinformatics technology can accelerate the discovery of drug targets, the screening of compounds, and the drug design process through efficient data analysis and computational simulation, it can not only greatly improve the efficiency and success rate of research and development, but also reduce the cost and risk compared with traditional medicine research. However, the application of bioinformatics in medicine research still faces challenges such as the difficulty in simulating the complexity of biology, the low accuracy of the algorithms of AI prediction models, the lack of high-quality bioinformatics data, and the representation of data for drug design. Therefore, we call for contributions from researchers with diverse backgrounds (e.g., biology and pharmaceutical science) and practices in bioinformatics that aim to address some of the mentioned challenges and difficulties in this field, as well as tools, techniques, and application studies. Both research and review articles proposing novel innovations are welcome.

Prof. Dr. Le Zhang
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. Biomolecules 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 2700 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

  • computer-aided drug design (CADD)
  • bioinformatics
  • systems medicine
  • drug discovery
  • computational 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

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24 pages, 3628 KiB  
Article
Dissecting the Emerging Regulatory and Mechanistic Paradigms of Transcribed Conserved Non-Coding Elements in Breast Cancer
by Wenyong Zhu, Hao Huang, Qiong Li, Yu Gu, Rongxin Zhang, Huiling Shu, Yunqi Zhao, Hongde Liu and Xiao Sun
Biomolecules 2025, 15(5), 627; https://doi.org/10.3390/biom15050627 (registering DOI) - 27 Apr 2025
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Abstract
Transcribed conserved non-coding elements (TCNEs), which are non-coding genomic elements that can regulate vital gene expression, play an unclear role in the development of severe diseases mainly associated with carcinogenesis. Currently, there are no mature tools for the identification of TCNEs. To compensate [...] Read more.
Transcribed conserved non-coding elements (TCNEs), which are non-coding genomic elements that can regulate vital gene expression, play an unclear role in the development of severe diseases mainly associated with carcinogenesis. Currently, there are no mature tools for the identification of TCNEs. To compensate for the lack of a systematic interpretation of the functional characterization and regulatory mechanisms of TCNE spatiotemporal activities, we developed a flexible pipeline, called captureTCNE, to depict the landscape of TCNEs and applied it to our breast cancer cohort (SEU-BRCA). Meanwhile, we investigated the genome-wide characteristics of TCNEs and unraveled that TCNEs harbor enhancer-like chromatin signatures as well as participate in the transcriptional machinery to regulate essential genes or architect biological regulatory networks of breast cancer. Specifically, the TCNE transcripts could recruit RBPs, such as ENOX1 and PTBP1, which are involved in gene expression regulation, to participate in the formation of regulatory networks and the association with altered splicing patterns. In particular, the presence of a non-classical secondary structure, called RNA G-quadruplex, on TCNE transcripts contributed to the recruitment of RBPs associated with subtype-specific transcriptional processes related to the estrogen response in breast cancer. Ultimately, we also analyzed the mutational signatures of variant-containing TCNEs and discerned twenty-one genes as essential components of the regulatory mechanism of TCNEs in breast cancer. Our study provides an effective TCNE identification pipeline and insights into the regulatory mechanisms of TCNEs in breast cancer, contributing to further knowledge of TCNEs and the emergence of innovative therapeutic strategies for breast cancer. Full article
(This article belongs to the Special Issue Application of Bioinformatics in Medicine)
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17 pages, 3203 KiB  
Article
XModNN: Explainable Modular Neural Network to Identify Clinical Parameters and Disease Biomarkers in Transcriptomic Datasets
by Jan Oldenburg, Jonas Wagner, Sascha Troschke-Meurer, Jessica Plietz, Lars Kaderali, Henry Völzke, Matthias Nauck, Georg Homuth, Uwe Völker and Stefan Simm
Biomolecules 2024, 14(12), 1501; https://doi.org/10.3390/biom14121501 - 25 Nov 2024
Viewed by 1338
Abstract
The Explainable Modular Neural Network (XModNN) enables the identification of biomarkers, facilitating the classification of diseases and clinical parameters in transcriptomic datasets. The modules within XModNN represent specific pathways or genes of a functional hierarchy. The incorporation of biological insights into the architectural [...] Read more.
The Explainable Modular Neural Network (XModNN) enables the identification of biomarkers, facilitating the classification of diseases and clinical parameters in transcriptomic datasets. The modules within XModNN represent specific pathways or genes of a functional hierarchy. The incorporation of biological insights into the architectural design reduced the number of parameters. This is further reinforced by the weighted multi-loss progressive training, which enables successful classification with a reduced number of replicates. The combination of this workflow with layer-wise relevance propagation ensures a robust post hoc explanation of the individual module contribution. Two use cases were employed to predict sex and neuroblastoma cell states, demonstrating that XModNN, in contrast to standard statistical approaches, results in a reduced number of candidate biomarkers. Moreover, the architecture enables the training on a limited number of examples, attaining the same performance and robustness as support vector machine and random forests. The integrated pathway relevance analysis improves a standard gene set overrepresentation analysis, which relies solely on gene assignment. Two crucial genes and three pathways were identified for sex classification, while 26 genes and six pathways are highly important to discriminate adrenergic–mesenchymal cell states in neuroblastoma cancer. Full article
(This article belongs to the Special Issue Application of Bioinformatics in Medicine)
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Review

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18 pages, 967 KiB  
Review
Application of Spatial Transcriptomics in Digestive System Tumors
by Bowen Huang, Yingjia Chen and Shuqiang Yuan
Biomolecules 2025, 15(1), 21; https://doi.org/10.3390/biom15010021 - 27 Dec 2024
Viewed by 1249
Abstract
In the field of digestive system tumor research, spatial transcriptomics technologies are used to delve into the spatial structure and the spatial heterogeneity of tumors and to analyze the tumor microenvironment (TME) and the inter-cellular interactions within it by revealing gene expression in [...] Read more.
In the field of digestive system tumor research, spatial transcriptomics technologies are used to delve into the spatial structure and the spatial heterogeneity of tumors and to analyze the tumor microenvironment (TME) and the inter-cellular interactions within it by revealing gene expression in tumors. These technologies are also instrumental in the diagnosis, prognosis, and treatment of digestive system tumors. This review provides a concise introduction to spatial transcriptomics and summarizes recent advances, application prospects, and technical challenges of these technologies in digestive system tumor research. This review also discusses the importance of combining spatial transcriptomics with single-cell RNA sequencing (scRNA-seq), artificial intelligence, and machine learning in digestive system cancer research. Full article
(This article belongs to the Special Issue Application of Bioinformatics in Medicine)
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