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Biomolecules
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Artificial Intelligence (AI) in Biomedicine: 2nd Edition
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Artificial Intelligence (AI) in Biomedicine: 2nd Edition

A special issue of Biomolecules (ISSN 2218-273X). This special issue belongs to the section "Molecular Medicine".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 1474

Special Issue Editor

Dr. Le Nguyen Quoc Khanh

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Guest Editor
Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei 106, Taiwan
Interests: artificial intelligence; bioinformatics; biomedical and healthcare informatics; genomics; medical imaging; proteomics; radiomics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recently, artificial intelligence (AI) algorithms have shown promising results and advantages in processing various aspects of data. With the assistance of fast-improving AI algorithms (from machine learning, deep learning, natural language processing, etc.), we can use highly efficient data-mining tools to handle a huge body of biomedicine databases. AI in biomedicine includes both basic (i.e., biological and physiological principles) as well as clinical research with information of many biomedical disciplines and areas of specialty. Through these applications, AI expects to help transform the world of medicine by training models that predict how the phenotype and genotype are defined and enables the exploration of biomedical diagnosis and therapies.

This Special Issue aims to provide a place covering the applications of AI to different aspects of biomedicine. Research areas may include (but are not limited to) the following:

  • Applications of AI in molecular biology and biochemistry
  • Applications of AI in bioinformatics and system biology
  • Applications of AI in genomics and genetics
  • Applications of AI in drug discovery and development
  • Applications of AI in biomedical imaging
  • Applications of AI in disease diagnosis and prognosis
  • Applications of AI in clinical decision support systems
  • Applications of AI in biomedical and health informatics
  • Big data analysis in biomedicine
  • Natural language processing in biomedical text mining

Dr. Le Nguyen Quoc Khanh
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

  • bioinformatics and biomedical informatics
  • biomedical image processing
  • cancer genomics
  • data analysis and big data
  • data mining and text mining
  • e-Health machine learning and deep learning
  • molecular biology and medicine
  • natural language processing
  • single-cell sequencing analysis

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Related Special Issue

Published Papers (1 paper)

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Review

21 pages, 1504 KiB  
Review
Utilizing Feature Selection Techniques for AI-Driven Tumor Subtype Classification: Enhancing Precision in Cancer Diagnostics
by Jihan Wang, Zhengxiang Zhang and Yangyang Wang
Biomolecules 2025, 15(1), 81; https://doi.org/10.3390/biom15010081 - 8 Jan 2025
Cited by 1 | Viewed by 1097
Abstract
Cancer’s heterogeneity presents significant challenges in accurate diagnosis and effective treatment, including the complexity of identifying tumor subtypes and their diverse biological behaviors. This review examines how feature selection techniques address these challenges by improving the interpretability and performance of machine learning (ML) [...] Read more.
Cancer’s heterogeneity presents significant challenges in accurate diagnosis and effective treatment, including the complexity of identifying tumor subtypes and their diverse biological behaviors. This review examines how feature selection techniques address these challenges by improving the interpretability and performance of machine learning (ML) models in high-dimensional datasets. Feature selection methods—such as filter, wrapper, and embedded techniques—play a critical role in enhancing the precision of cancer diagnostics by identifying relevant biomarkers. The integration of multi-omics data and ML algorithms facilitates a more comprehensive understanding of tumor heterogeneity, advancing both diagnostics and personalized therapies. However, challenges such as ensuring data quality, mitigating overfitting, and addressing scalability remain critical limitations of these methods. Artificial intelligence (AI)-powered feature selection offers promising solutions to these issues by automating and refining the feature extraction process. This review highlights the transformative potential of these approaches while emphasizing future directions, including the incorporation of deep learning (DL) models and integrative multi-omics strategies for more robust and reproducible findings. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) in Biomedicine: 2nd Edition)
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