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Algorithms
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Advanced Algorithms for Biomedical Data Analysis
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Advanced Algorithms for Biomedical Data Analysis

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: 28 February 2026 | Viewed by 1459

Special Issue Editor

Dr. Micheal Olaolu Arowolo

E-Mail Website
Guest Editor
Department of Public Health Sciences, Health Informatics Program, Xavier University of Louisiana, New Orleans, LA 70461, USA
Interests: bioinformatics; artificial intelligence; machine learning; data science; computing

Special Issue Information

Dear Colleagues,

Advances in genetics, medical imaging, wearable technology, and electronic health records have all contributed to the swift growth of biomedical data, necessitating the development of novel computational techniques for the analysis and interpretation of these complex data. The goal of this Special Issue is to present state-of-the-art research on sophisticated algorithms focused on addressing the unique difficulties of biological data processing. Topics of interest include, but are not restricted to, the following:

  • Deep learning and machine learning models for the diagnosis and prognosis of diseases.
  • Algorithms for analysing proteomic and genomic data.
  • Computational techniques for processing and analysing medical images.
  • Methods of data fusion and integration for multi-omics and multi-modal data.
  • Interpretability and explainable AI in biomedical applications.
  • Large-scale biomedical datasets benefitting from scalable techniques.
  • Clinical text analysis using natural language processing (NLP) applications.

Original research articles, reviews, and case studies showcasing cutting-edge techniques, resources, and applications in this rapidly developing area are encouraged. This Special Issue will showcase the revolutionary potential of sophisticated algorithms in enhancing healthcare outcomes and furthering biomedical research through submissions from scholars and practitioners.

Dr. Micheal Olaolu Arowolo
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. Algorithms 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 1800 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

  • biomedical data analysis
  • machine learning in healthcare
  • genomic and proteomic data
  • medical image processing
  • explainable AI in biomedicine
  • multi-omics data integration
  • clinical NLP

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

Published Papers (2 papers)

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Research

11 pages, 1220 KB  
Article
A Method for Calculating Whole-Genome Sequencing Outcomes from Trio Data
by Nikita Koltunov, Egor Guguchkin, Oleg Samovarov, Liudmila Mikhailova and Evgeny Karpulevich
Algorithms 2025, 18(10), 610; https://doi.org/10.3390/a18100610 - 29 Sep 2025
Viewed by 294
Abstract
Background. Whole-genome sequencing (WGS) enables comprehensive detection of genetic variants but faces limitations in benchmarking due to incomplete reference datasets. Trio-based analysis, leveraging Mendelian inheritance, provides an alternative strategy for validating sequencing results and estimating error rates, particularly in regulatory genomic regions. [...] Read more.
Background. Whole-genome sequencing (WGS) enables comprehensive detection of genetic variants but faces limitations in benchmarking due to incomplete reference datasets. Trio-based analysis, leveraging Mendelian inheritance, provides an alternative strategy for validating sequencing results and estimating error rates, particularly in regulatory genomic regions. Methods. We extended the nf-core/sarek WGS pipeline by integrating a module that collects parental and offspring allele information, extracts regulatory genomic regions, and computes Mendelian-consistency scores. The algorithm processes variant calls from parents and children to identify expected versus anomalous inheritance patterns. The module was implemented in C++ and integrated into the Nextflow workflow, supporting automated analysis of trio datasets. Results. The method was validated on two real trio datasets, comparing DeepVariant and HaplotypeCaller as variant callers. For both trios, DeepVariant consistently achieved higher sensitivity and precision, with statistically significant differences confirmed using 95% confidence intervals. These results demonstrate that the proposed approach enables effective benchmarking of variant-calling performance in non-benchmark datasets. Conclusions. The developed method provides a practical and scalable framework for quantifying WGS outcomes from trio data. By incorporating Mendelian-inheritance validation into existing pipelines, researchers can estimate sequencing error rates, compare variant callers, and optimize workflows in regulatory genomic regions. Our findings confirm the superior performance of DeepVariant over HaplotypeCaller for the studied datasets. Full article
(This article belongs to the Special Issue Advanced Algorithms for Biomedical Data Analysis)
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24 pages, 2159 KB  
Article
Agentic RAG-Driven Multi-Omics Analysis for PI3K/AKT Pathway Deregulation in Precision Medicine
by Micheal Olaolu Arowolo, Sulaiman Olaniyi Abdulsalam, Rafiu Mope Isiaka, Kingsley Theophilus Igulu, Bukola Fatimah Balogun, Mihail Popescu and Dong Xu
Algorithms 2025, 18(9), 545; https://doi.org/10.3390/a18090545 - 30 Aug 2025
Viewed by 701
Abstract
The phosphoinositide 3-kinase (PI3K)/AKT signaling pathway is a crucial regulator of cellular metabolism, proliferation, and survival. It is frequently dysregulated in metabolic, cardiovascular, and neoplastic disorders. Despite the advancements in multi-omics technology, existing methods often fail to provide real-time, pathway-specific insights for precision [...] Read more.
The phosphoinositide 3-kinase (PI3K)/AKT signaling pathway is a crucial regulator of cellular metabolism, proliferation, and survival. It is frequently dysregulated in metabolic, cardiovascular, and neoplastic disorders. Despite the advancements in multi-omics technology, existing methods often fail to provide real-time, pathway-specific insights for precision medicine and drug repurposing. We offer Agentic RAG-Driven Multi-Omics Analysis (ARMOA), an autonomous, hypothesis-driven system that integrates retrieval-augmented generation (RAG), large language models (LLMs), and agentic AI to thoroughly analyze genomic, transcriptomic, proteomic, and metabolomic data. Through the use of graph neural networks (GNNs) to model complex interactions within the PI3K/AKT pathway, ARMOA enables the discovery of novel biomarkers, probable candidates for drug repurposing, and customized therapy responses to address the complexities of PI3K/AKT dysregulation in disease states. ARMOA dynamically gathers and synthesizes knowledge from multiple sources, including KEGG, TCGA, and DrugBank, to guarantee context-aware insights. Through adaptive reasoning, it gradually enhances predictions, achieving 91% accuracy in external testing and 92% accuracy in cross-validation. Case studies in breast cancer and type 2 diabetes demonstrate that ARMOA can identify synergistic drug combinations with high clinical relevance and predict therapeutic outcomes specific to each patient. The framework’s interpretability and scalability are greatly enhanced by its use of multi-omics data fusion and real-time hypothesis creation. ARMOA provides a cutting-edge example for precision medicine by integrating multi-omics data, clinical judgment, and AI agents. Its ability to provide valuable insights on its own makes it a powerful tool for advancing biomedical research and treatment development. Full article
(This article belongs to the Special Issue Advanced Algorithms for Biomedical Data Analysis)
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