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New Insights in Translational Bioinformatics: Second Edition
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New Insights in Translational Bioinformatics: Second 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: 20 August 2025 | Viewed by 4322

Special Issue Editor

Dr. Camelia Quek

E-Mail Website
Guest Editor
Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia
Interests: cancer; immunotherapy; gene expression profiling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is a continuation of our previous one, entitled "New Insights in Translational Bioinformatics" (https://www.mdpi.com/journal/ijms/special_issues/EV256YI5EF).

Translational bioinformatics is an emerging field that focusses on providing the efficient translation of basic science (molecular, genetic, and cellular) and clinical data into clinical practice. As a multidisciplinary field of science, translational bioinformatics combines biology, computer science, data engineering, mathematics, statistics, and medicine to analyse and interpret the molecular basis of disease that has strong implications in clinical products or human health. New technologies and the increasingly voluminous amounts of scientific and clinical data are posing challenges in different fields, such as single-cell analysis, spatial image analysis, genomics, proteomics, transcriptomics, multi-omics data analysis, the multimodal integration of molecular and clinical data, drug discovery, personalised medicine, and biomarkers. In this Special Issue, manuscripts focusing on topics including, but not limited to, single-cell sequencing, spatial imaging, machine learning, artificial intelligence, next-generation sequencing for personalised medicine and biomarkers, rare and common variants of disease, drug discovery, system biology, pharmacogenomics, biomedical data mining, and data visualisation are invited. Original research articles, reviews, opinions/commentaries, perspectives, and short communications are welcome to be submitted to this Special Issue.

Dr. Camelia Quek
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

  • translational research
  • sequencing
  • single cell
  • spatial biology
  • treatment
  • diagnostics
  • machine learning
  • artificial intelligence
  • model
  • computational biology

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Published Papers (3 papers)

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Research

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17 pages, 3604 KiB  
Article
Integrating Machine Learning and Follow-Up Variables to Improve Early Detection of Hepatocellular Carcinoma in Tyrosinemia Type 1: A Multicenter Study
by Karen Fuenzalida, María Jesús Leal-Witt, Alejandro Acevedo, Manuel Muñoz, Camila Gudenschwager, Carolina Arias, Juan Francisco Cabello, Giancarlo La Marca, Cristiano Rizzo, Andrea Pietrobattista, Marco Spada, Carlo Dionisi-Vici and Verónica Cornejo
Int. J. Mol. Sci. 2025, 26(8), 3839; https://doi.org/10.3390/ijms26083839 - 18 Apr 2025
Viewed by 188
Abstract
Hepatocellular carcinoma (HCC) is a major complication of tyrosinemia type 1 (HT-1), an inborn error of metabolism affecting tyrosine catabolism. The risk of HCC is higher in late diagnoses despite treatment. Alpha-fetoprotein (AFP) is widely used to detect liver cancer but has limitations [...] Read more.
Hepatocellular carcinoma (HCC) is a major complication of tyrosinemia type 1 (HT-1), an inborn error of metabolism affecting tyrosine catabolism. The risk of HCC is higher in late diagnoses despite treatment. Alpha-fetoprotein (AFP) is widely used to detect liver cancer but has limitations in early-stage HCC detection. This study aimed to implement a machine-learning (ML) approach to identify the most relevant laboratory variables to predict AFP alteration using constrained multidimensional data from Chilean and Italian HT-1 cohorts. A longitudinal retrospective study analyzed 219 records from 35 HT-1 patients, including 8 with HCC and 5 diagnosed through newborn screening. The dataset contained biochemical and demographic variables that were analyzed using the eXtreme Gradient Boosting algorithm, which was trained to predict abnormal AFP levels (>5 ng/mL). Four key variables emerged as significant predictors: alanine transaminase (ALT), alkaline phosphatase, age at diagnosis, and current age. ALT emerged as the most promising indicator of AFP alteration, potentially preceding AFP level changes and improving HCC detection specificity at a cut-off value of 29 UI/L (AUROC = 0.73). Despite limited data from this rare disease, the ML approach successfully analyzed follow-up biomarkers, identifying ALT as an early predictor of AFP elevation and a potential biomarker for HCC progression. Full article
(This article belongs to the Special Issue New Insights in Translational Bioinformatics: Second Edition)
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Review

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28 pages, 1048 KiB  
Review
Single-Cell Multi-Omics: Insights into Therapeutic Innovations to Advance Treatment in Cancer
by Angel Guan and Camelia Quek
Int. J. Mol. Sci. 2025, 26(6), 2447; https://doi.org/10.3390/ijms26062447 - 9 Mar 2025
Viewed by 1542
Abstract
Advances in single-cell multi-omics technologies have deepened our understanding of cancer biology by integrating genomic, transcriptomic, epigenomic, and proteomic data at single-cell resolution. These single-cell multi-omics technologies provide unprecedented insights into tumour heterogeneity, tumour microenvironment, and mechanisms of therapeutic resistance, enabling the development [...] Read more.
Advances in single-cell multi-omics technologies have deepened our understanding of cancer biology by integrating genomic, transcriptomic, epigenomic, and proteomic data at single-cell resolution. These single-cell multi-omics technologies provide unprecedented insights into tumour heterogeneity, tumour microenvironment, and mechanisms of therapeutic resistance, enabling the development of precision medicine strategies. The emerging field of single-cell multi-omics in genomic medicine has improved patient outcomes. However, most clinical applications still depend on bulk genomic approaches, which fail to directly capture the genomic variations driving cellular heterogeneity. In this review, we explore the common single-cell multi-omics platforms and discuss key analytical steps for data integration. Furthermore, we highlight emerging knowledge in therapeutic resistance and immune evasion, and the potential of new therapeutic innovations informed by single-cell multi-omics. Finally, we discuss the future directions of the application of single-cell multi-omics technologies. By bridging the gap between technological advancements and clinical implementation, this review provides a roadmap for leveraging single-cell multi-omics to improve cancer treatment and patient outcomes. Full article
(This article belongs to the Special Issue New Insights in Translational Bioinformatics: Second Edition)
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18 pages, 558 KiB  
Review
Spatial Transcriptomics in Human Cardiac Tissue
by Quynh Nguyen, Lin Wei Tung, Bruce Lin, Raam Sivakumar, Funda Sar, Gurpreet Singhera, Ying Wang, Jeremy Parker, Stephane Le Bihan, Amrit Singh, Fabio M.V. Rossi, Colin Collins, Jamil Bashir and Zachary Laksman
Int. J. Mol. Sci. 2025, 26(3), 995; https://doi.org/10.3390/ijms26030995 - 24 Jan 2025
Viewed by 2007
Abstract
Spatial transcriptomics has transformed our understanding of gene expression by preserving the spatial context within tissues. This review focuses on the application of spatial transcriptomics in human cardiac tissues, exploring current technologies with a focus on commercially available platforms. We also highlight key [...] Read more.
Spatial transcriptomics has transformed our understanding of gene expression by preserving the spatial context within tissues. This review focuses on the application of spatial transcriptomics in human cardiac tissues, exploring current technologies with a focus on commercially available platforms. We also highlight key studies utilizing spatial transcriptomics to investigate cardiac development, electro-anatomy, immunology, and ischemic heart disease. These studies demonstrate how spatial transcriptomics can be used in conjunction with other omics technologies to provide a more comprehensive picture of human health and disease. Despite its transformative potential, spatial transcriptomics comes with several challenges that limit its widespread adoption and broader application. By addressing these limitations and fostering interdisciplinary collaboration, spatial transcriptomics has the potential to become an essential tool in cardiovascular research. We hope this review serves as a practical guide for researchers interested in adopting spatial transcriptomics, particularly those with limited prior experience, by providing insights into current technologies, applications, and considerations for successful implementation. Full article
(This article belongs to the Special Issue New Insights in Translational Bioinformatics: Second Edition)
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