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Machine Learning Applications in Bioinformatics and Biomedicine: 3rd Edition
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Machine Learning Applications in Bioinformatics and Biomedicine: 3rd Edition

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 September 2025 | Viewed by 84

Special Issue Editor

Dr. Hao Lv

E-Mail Website
Guest Editor
Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: focused on developing machine learning/deep learning tools for identifying DNA, RNA, and protein modification sites, with current interest in developing computational pipelines to generate embeddings and identify cell types from single-cell Hi-C data
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Machine learning has been evolving for over 40 years. In recent years, with the rapid accumulation of data in the biological and medical fields, machine learning has seen a wide range of applications. Our aim in launching this Special Issue is to provide a platform to share the latest cutting-edge work related to applications of machine learning in the biological and medicine fields and to promote the development of related work. This Special Issue will focus on various aspects of the development and application of computational methods and techniques in biological and medical data for discovering disease markers. Topics of interest include, but are not limited to, the following:

  • The identification of disease markers from the genome, transcriptome, proteome and metabolome;
  • The discovery of drug targets using machine learning;
  • Drug design based on machine learning;
  • Using machine learning to analyze clinical data;
  • Research on big data from physical examinations based on machine learning and artificial intelligence;
  • The prediction of drug side effects based on machine learning;
  • The discovery of epigenetic markers for diseases using artificial intelligence;
  • The discovery of molecular network markers for disease diagnosis and therapy;
  • Early screening for diseases based on artificial intelligence.

Dr. Hao Lv
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

  • genome
  • transcriptome
  • proteome
  • metabolome
  • drug target
  • machine learning
  • prediction of drug side effects
  • epigenetic marker discovery for diseases
  • molecular network marker
  • early screening of diseases

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Published Papers (1 paper)

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Research

28 pages, 4662 KiB  
Article
XGB-BIF: An XGBoost-Driven Biomarker Identification Framework for Detecting Cancer Using Human Genomic Data
by Veena Ghuriani, Jyotsna Talreja Wassan, Priyal Tripathi and Anshika Chauhan
Int. J. Mol. Sci. 2025, 26(12), 5590; https://doi.org/10.3390/ijms26125590 - 11 Jun 2025
Abstract
The human genome has a profound impact on human health and disease detection. Carcinoma (cancer) is one of the prominent diseases that majorly affect human health and requires the development of different treatment strategies and targeted therapies based on effective disease detection. Therefore, [...] Read more.
The human genome has a profound impact on human health and disease detection. Carcinoma (cancer) is one of the prominent diseases that majorly affect human health and requires the development of different treatment strategies and targeted therapies based on effective disease detection. Therefore, our research aims to identify biomarkers associated with distinct cancer types (gastric, lung, and breast) using machine learning. In the current study, we have analyzed the human genomic data of gastric cancer, breast cancer, and lung cancer patients using XGB-BIF (i.e., XGBoost-Driven Biomarker Identification Framework for detecting cancer). The proposed framework utilizes feature selection via XGBoost (eXtreme Gradient Boosting), which captures feature interactions efficiently and takes care of the non-linear effects in the genomic data. The research progressed by training XGBoost on the full dataset, ranking the features based on the Gain measure (importance), followed by the classification phase, which employed support vector machines (SVM), logistic regression (LR), and random forest (RF) models for classifying cancer-diseased and non-diseased states. To ensure interpretability and transparency, we also applied SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), enabling the identification of high-impact biomarkers contributing to risk stratification. Biomarker significance is discussed primarily via pathway enrichment and by studying survival analysis (Kaplan–Meier curves, Cox regression) for identified biomarkers to strengthen translational value. Our models achieved high predictive performance, with an accuracy of more than 90%, to classify and link genomic data into diseased (cancer) and non-diseased states. Furthermore, we evaluated the models using Cohen’s Kappa statistic, which confirmed strong agreement between predicted and actual risk categories, with Kappa scores ranging from 0.80 to 0.99. Our proposed framework also achieved strong predictions on the METABRIC dataset during external validation, attaining an AUC-ROC of 93%, accuracy of 0.79%, and Kappa of 74%. Through extensive experimentation, XGB-BIF identified the top biomarker genes for different cancer datasets (gastric, lung, and breast). CBX2, CLDN1, SDC2, PGF, FOXS1, ADAMTS18, POLR1B, and PYCR3 were identified as important biomarkers to identify diseased and non-diseased states of gastric cancer; CAVIN2, ADAMTS5, SCARA5, CD300LG, and GIPC2 were identified as important biomarkers for breast cancer; and CLDN18, MYBL2, ASPA, AQP4, FOLR1, and SLC39A8 were identified as important biomarkers for lung cancer. XGB-BIF could be utilized for identifying biomarkers of different cancer types using genetic data, which can further help clinicians in developing targeted therapies for cancer patients. Full article
(This article belongs to the Special Issue Machine Learning Applications in Bioinformatics and Biomedicine: 3rd Edition)
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