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Life
Special Issues
Bioinformatics Tools and Machine Learning Methods for Biomarker Discovery
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Bioinformatics Tools and Machine Learning Methods for Biomarker Discovery

A special issue of Life (ISSN 2075-1729). This special issue belongs to the section "Radiobiology and Nuclear Medicine".

Deadline for manuscript submissions: closed (31 December 2024) | Viewed by 1523

Special Issue Editors

Dr. Trang Do

E-Mail Website
Guest Editor
School of Innovation, Design and Technology, Wellington Institute of Technology, Wellington 5012, New Zealand
Interests: machine learning; deep learning; data visualization; health informatics; drug discovery; natural language processing; intelligent systems
Special Issues, Collections and Topics in MDPI journals
Dr. Binh P. Nguyen

E-Mail Website
Guest Editor
School of Mathematics and Statistics, Victoria University of Wellington, Wellington 6012, New Zealand
Interests: machine learning; data science; deep learning; biomedical image analysis; health informatics; bioinformatics; drug discovery
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to compile cutting-edge research and advancements in bioinformatics tools and machine learning methods specifically tailored for the discovery and validation of biomarkers in various diseases and health conditions. Biomarkers play a pivotal role in disease diagnosis, prognosis, treatment selection, and monitoring, and this Special Issue seeks to spotlight the innovative methodologies and computational approaches driving biomarker discovery. This Special Issue welcomes diverse contributions from researchers and experts across bioinformatics, computational biology, health informatics, and related domains to share their original research, methodologies, reviews, and perspectives on themes including machine learning in biomarker discovery, multi-omics integration, NGS data analysis, single-cell omics, the clinical validation of biomarkers, and addressing challenges and future directions in biomarker discovery.

Dr. Trang Do
Dr. Binh P. Nguyen
Guest Editors

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. Life 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
  • machine learning
  • deep learning
  • biomarkers
  • multi-omics
  • NGS
  • single-cell
  • gene expression

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

Published Papers (1 paper)

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20 pages, 1569 KiB  
Systematic Review
A Review of Artificial Intelligence-Based Down Syndrome Detection Techniques
by Mujeeb Ahmed Shaikh, Hazim Saleh Al-Rawashdeh and Abdul Rahaman Wahab Sait
Life 2025, 15(3), 390; https://doi.org/10.3390/life15030390 - 1 Mar 2025
Viewed by 898
Abstract
Background: Down syndrome (DS) is one of the most prevalent chromosomal abnormalities affecting global healthcare. Recent advances in artificial intelligence (AI) and machine learning (ML) have enhanced DS diagnostic accuracy. However, there is a lack of thorough evaluations analyzing the overall impact and [...] Read more.
Background: Down syndrome (DS) is one of the most prevalent chromosomal abnormalities affecting global healthcare. Recent advances in artificial intelligence (AI) and machine learning (ML) have enhanced DS diagnostic accuracy. However, there is a lack of thorough evaluations analyzing the overall impact and effectiveness of AI-based DS diagnostic approaches. Objectives: This review intends to identify methodologies and technologies used in AI-driven DS diagnostics. It evaluates the performance of AI models in terms of standard evaluation metrics, highlighting their strengths and limitations. Methodology: In order to ensure transparency and rigor, the authors followed the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. They extracted 1175 articles from major academic databases. By leveraging inclusion and exclusion criteria, a final set of 25 articles was selected. Outcomes: The findings revealed significant advancements in AI-powered DS diagnostics across diverse data modalities. The modalities, including facial images, ultrasound scans, and genetic data, demonstrated strong potential for early DS diagnosis. Despite these advancements, this review outlined the limitations of AI approaches. Small and imbalanced datasets reduce the generalizability of the AI models. The authors present actionable strategies to enhance the clinical adoptions of these models. Full article
(This article belongs to the Special Issue Bioinformatics Tools and Machine Learning Methods for Biomarker Discovery)
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