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Bioinformatics Study in Human Diseases: Integration of Omics Data for Personalized Medicine (Third 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: 31 May 2026 | Viewed by 648

Special Issue Editor

Special Issue Information

Dear Colleagues,

In the past decade, unprecedented breakthroughs have emerged from the application of next-generation sequencing technologies and advanced artificial intelligence methods. These advances have created remarkable opportunities for identifying key companion biomarkers and biosignatures, improving diagnosic efficiency, enhancing prognostic prediction, accelerating drug discovery, and uncovering potential mechanisms in a comprehensive, multi-omics-driven manner.

Bioinformatics is widely employed to address these challenges. A variety of bioinformatics tools and open access datasets have been developed to integrate omics data—including epigenomics, metabolomics, transcriptomics, and genomics—comprehensively. Compared withe traditional approaches, bioinformatics has considerably improved diagnostic and prognostic accuracy, expedited the discovery of novel drug targets, and deepened the functional understanding of disease mechanisms, paving the way toward advances in precision medicine.

We are pleased to invite you to contribute original research articles related to (but not limited to) bioinformatics-centric identification of biomarkers, biosignatures, drug candidates, mechanisms, or predictive algorithms for diagnosis, prognosis, and potential therapeutics in human diseases. Critical review manuscripts offering forward-looking perspectives and outlining future research directions are also especially welcome.

Please kindly note that new methods must be compared with existing state-of-the-art approaches using real biological datasets. The inclusion of experimental validation is highly encouraged.

We look forward to receiving your contributions.

Dr. Hung-Yu Lin
Guest Editor

Manuscript Submission Information

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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.

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Keywords

  • cancers
  • ageing-related diseases
  • bioinformatics
  • multi-omics
  • pharmacogenetics
  • deep learning
  • evolutionary learning
  • diagnosis
  • prognosis
  • companion biomarker
  • drug screening
  • mechanism dissection

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Published Papers (1 paper)

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Research

12 pages, 868 KB  
Article
Enhanced Detection of BRCA Copy Number Alterations Within a Commercial HRD Assay: Implications for Precision Oncology in Ovarian Cancer
by Maria De Bonis, Pierluigi Iapicca, Elisa De Paolis, Francesca Brisighelli, Jessica Evangelista, Alessia Perrucci, Claudio Ricciardi Tenore, Giulia Maneri, Paola Concolino, Alessia Piermattei, Iolanda Mozzetta, Tina Pasciuto, Alessia Preziosi, Luciano Giacò, Simona Duranti, Camilla Nero, Anna Fagotti and Angelo Minucci
Int. J. Mol. Sci. 2026, 27(9), 3965; https://doi.org/10.3390/ijms27093965 - 29 Apr 2026
Viewed by 409
Abstract
Large genomic rearrangements (LGRs), occurring as copy number alterations (CNAs), represent a clinically relevant class of pathogenic or likely pathogenic variants (P LPVs) in BRCA1/2 (BRCA) genes in ovarian cancer (OC). We evaluated the performance of a high-resolution algorithm integrated into [...] Read more.
Large genomic rearrangements (LGRs), occurring as copy number alterations (CNAs), represent a clinically relevant class of pathogenic or likely pathogenic variants (P LPVs) in BRCA1/2 (BRCA) genes in ovarian cancer (OC). We evaluated the performance of a high-resolution algorithm integrated into a commercial homologous recombination deficiency (HRD) assay to improve the identification of clinically actionable CNAs in BRCA genes by formalin-fixed paraffin-embedded (FFPE) samples. A total of 760 OC samples were analyzed using a commercial HRD assay incorporating a bioinformatics algorithm for CNA detection. The algorithm was additionally applied to additional homologous recombination repair (HRR) genes, and associations between CNA events and genomic instability (GI) were evaluated. The algorithm demonstrated high sensitivity for both gene and exon-level CNA. The high correlation between CNA positivity cases and GI, in the absence of P/LPVs BRCA single-nucleotide or indels variants, emphasizes the value of integrating CNA detection into routine HRD testing workflows. The extended analysis of additional HRR genes enabled broader characterization of clinically relevant CNAs. This study enables reliable identification of clinically relevant BRCA LGRs from FFPE within HRD testing, supporting a tumor-first diagnostic strategy. This approach may expand the identification of OC patients potentially eligible for PARP inhibitor therapy. Full article
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