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Biology
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Computational Discovery Tools in Genomics and Precision Medicine
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Computational Discovery Tools in Genomics and Precision Medicine

A special issue of Biology (ISSN 2079-7737). This special issue belongs to the section "Bioinformatics".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 1089

Special Issue Editor

Dr. Felix Dietlein

E-Mail Website
Guest Editor
1. Computational Health Informatics Program, Boston Children’s Hospital, Harvard Medical School, Harvard University, Boston, MA 02115, USA
2. Division of Population Sciences, Department of Medical Oncology, Dana-Farber Cancer Institute, Havard Medical School, Cancer Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
Interests: computational genomics; data-driven drug design; precision medicine

Special Issue Information

Dear Colleagues,

Over the past decade, data science has transformed from a supportive discipline into one of the primary driving forces for functional discoveries in biological, genomic, and medical disciplines. This paradigm shift has been marked by recent computational innovations that now allow us to address long-standing fundamental questions in genomics and biology. As a result, biologically informed algorithms are no longer supplementary in precision medicine today, but rather, they serve as essential engines propelling new biological insights and innovations.

We are pleased to invite you to contribute to our Special Issue on “Computational Discovery Tools in Genomics and Precision Medicine” in the Bioinformatics section. While contributions across the entire field of computational biology are welcome, special consideration will be given to manuscripts with a clear emphasis on biologically driven algorithms and data-driven approaches for novel biological and genomic discoveries. We are particularly interested in research that demonstrates the use and development of innovative computational approaches to unlock novel biological insights in fields/mechanisms/diseases where functional and experimental data are limited, and data-driven approaches are essential for addressing this challenge. Papers highlighting the use of large-scale data integration, machine learning, and predictive modeling to generate impactful biological findings will be prioritized. All manuscripts should report on new methods and include a section on benchmarking or validation, as well as a discussion of the biological significance.

We look forward to receiving your contributions.

Dr. Felix Dietlein
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. Biology 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

  • computational genomics
  • data-driven precision medicine
  • computational biology
  • biologically informed algorithms

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Published Papers (2 papers)

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Research

19 pages, 5990 KiB  
Article
FPCAM: A Weighted Dictionary-Driven Model for Single-Cell Annotation in Pulmonary Fibrosis
by Guojun Liu, Yan Shi, Hongxu Huang, Ningkun Xiao, Chuncheng Liu, Hongyu Zhao, Yongqiang Xing and Lu Cai
Biology 2025, 14(5), 479; https://doi.org/10.3390/biology14050479 - 26 Apr 2025
Viewed by 88
Abstract
The groundbreaking development of scRNA-seq has significantly improved cellular resolution. However, accurate cell-type annotation remains a major challenge. Existing annotation tools are often limited by their reliance on reference datasets, the heterogeneity of marker genes, and subjective biases introduced through manual intervention, all [...] Read more.
The groundbreaking development of scRNA-seq has significantly improved cellular resolution. However, accurate cell-type annotation remains a major challenge. Existing annotation tools are often limited by their reliance on reference datasets, the heterogeneity of marker genes, and subjective biases introduced through manual intervention, all of which impact annotation accuracy and reliability. To address these limitations, we developed FPCAM, a fully automated pulmonary fibrosis cell-type annotation model. Built on the R Shiny platform, FPCAM utilizes a matrix of up-regulated marker genes and a manually curated gene–cell association dictionary specific to pulmonary fibrosis. It achieves accurate and efficient cell-type annotation through similarity matrix construction and optimized matching algorithms. To evaluate its performance, we compared FPCAM with state-of-the-art annotation models, including SCSA, SingleR, and SciBet. The results showed that FPCAM and SCSA both achieved an accuracy of 89.7%, outperforming SingleR and SciBet. Furthermore, FPCAM demonstrated high accuracy in annotating the external validation dataset GSE135893, successfully identifying multiple cell subtypes. In summary, FPCAM provides an efficient, flexible, and accurate solution for cell-type identification and serves as a powerful tool for scRNA-seq research in pulmonary fibrosis and other related diseases. Full article
(This article belongs to the Special Issue Computational Discovery Tools in Genomics and Precision Medicine)
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23 pages, 5785 KiB  
Article
Uncovering New Biomarkers for Prostate Cancer Through Proteomic and Network Analysis
by Rossana Rossi, Elena Monica Borroni, Ishak Yusuf, Andrea Lomagno, Mohamed A. A. A. Hegazi, Pietro Luigi Mauri, Fabio Grizzi, Gianluigi Taverna and Dario Di Silvestre
Biology 2025, 14(3), 256; https://doi.org/10.3390/biology14030256 - 4 Mar 2025
Viewed by 774
Abstract
Background: Prostate cancer (PCa), is the second most prevalent solid tumor among men worldwide (7.3%), and the leading non-skin cancer in USA where it represents 14.9% of all new cancer cases diagnosed in 2024. This multifactorial disease exhibits substantial variation in incidence and [...] Read more.
Background: Prostate cancer (PCa), is the second most prevalent solid tumor among men worldwide (7.3%), and the leading non-skin cancer in USA where it represents 14.9% of all new cancer cases diagnosed in 2024. This multifactorial disease exhibits substantial variation in incidence and mortality across different ethnic groups and geographic regions. Although prostate-specific antigen (PSA) remains widely used as a biomarker for PCa, its limitations reduce its effectiveness for accurate detection. Consequently, finding molecules that can either complement PSA and other biomarkers is a major goal in PCa research. Methods: Urine samples were collected from healthy donors (n = 5) and patients with low- and high-risk PCa (4 and 7 subjects, respectively) and were analyzed using proteomic data-derived systems and biology approaches. The most promising proteins were further investigated by means of The Cancer Genome Atlas (TCGA) database to assess their associations with clinical and histopathological characteristics in a larger in silico patient population. Results: By evaluating the variations in the urinary proteome as a mirror of the changes occurring in prostate tumor tissue, components of complement and coagulation cascades and glutathione metabolism emerged as hallmarks of low- and high-risk PCa patients, respectively. Moreover, our integrated approach highlighted new potential biomarkers, including CPM, KRT8, ITIH2, and RCN1. Conclusions: The good overlap of our results with what is already reported in the literature supports the new findings in the perspective of improving the knowledge on PCa. Furthermore, they increase the panel of biomarkers that could enhance PCa management. Of course, further investigations on larger patient cohorts are required. Full article
(This article belongs to the Special Issue Computational Discovery Tools in Genomics and Precision Medicine)
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