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Bioinformatics and Computational Genomics

A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Human Genomics and Genetic Diseases".

Deadline for manuscript submissions: 5 May 2025 | Viewed by 699

Special Issue Editor

Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, ON, Canada
Interests: bioinformatics; genome; cancer; big data; human complex disease; machine learning/deep learning; NGS; variant annotation and interpretation

Special Issue Information

Dear Colleagues,

Bioinformatics, as an interdisciplinary field, has extensively covered the application of computer science, information, mathematics and computing to biological and clinical scenarios in recent decades. Now, the volume of genome data is exponentially increasing due to the application of sequencing methods such as next-generation technologies. These enormous volumes of genetic data need to be properly analysed and interpreted. Computational genomics and bioinformatics are efficient methods by which to extract biological information from complex genomic data.

This Special Issue focuses on recent developments or advances in computational genomics and bioinformatics methods in biology for the clinical field. It aims to provide both breadth in its diversity and depth in its consideration of cutting-edge techniques for computational genomics and bioinformatics.

We welcome the submission of research articles addressing significant research findings and novel methods/tools, as well as reviews in bioinformatics and computational genomics.

Dr. Quan Li
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. Genes 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
  • computational genomics
  • computational biology
  • genomics, genome annotation
  • sequence alignment, variant
  • metagenomic
  • high-throughput sequencing
  • single-cell sequencing
  • gene expression analysis
  • multi-dimensional omics
  • systems biology
  • evolutionary and phylogenetic analysis
  • AI and machine learning methods in bioinformatics

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Published Papers (1 paper)

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Review

15 pages, 557 KiB  
Review
Federated Learning: Breaking Down Barriers in Global Genomic Research
by Giulia Calvino, Cristina Peconi, Claudia Strafella, Giulia Trastulli, Domenica Megalizzi, Sarah Andreucci, Raffaella Cascella, Carlo Caltagirone, Stefania Zampatti and Emiliano Giardina
Genes 2024, 15(12), 1650; https://doi.org/10.3390/genes15121650 (registering DOI) - 22 Dec 2024
Abstract
Recent advancements in Next-Generation Sequencing (NGS) technologies have revolutionized genomic research, presenting unprecedented opportunities for personalized medicine and population genetics. However, issues such as data silos, privacy concerns, and regulatory challenges hinder large-scale data integration and collaboration. Federated Learning (FL) has emerged as [...] Read more.
Recent advancements in Next-Generation Sequencing (NGS) technologies have revolutionized genomic research, presenting unprecedented opportunities for personalized medicine and population genetics. However, issues such as data silos, privacy concerns, and regulatory challenges hinder large-scale data integration and collaboration. Federated Learning (FL) has emerged as a transformative solution, enabling decentralized data analysis while preserving privacy and complying with regulations such as the General Data Protection Regulation (GDPR). This review explores the potential use of FL in genomics, detailing its methodology, including local model training, secure aggregation, and iterative improvement. Key challenges, such as heterogeneous data integration and cybersecurity risks, are examined alongside regulations like GDPR. In conclusion, successful implementations of FL in global and national initiatives demonstrate its scalability and role in supporting collaborative research. Finally, we discuss future directions, including AI integration and the necessity of education and training, to fully harness the potential of FL in advancing precision medicine and global health initiatives. Full article
(This article belongs to the Special Issue Bioinformatics and Computational Genomics)
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