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Biology
Special Issues
Methods in Bioinformatics and Computational Biology
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Methods in Bioinformatics and Computational Biology

A special issue of Biology (ISSN 2079-7737).

Deadline for manuscript submissions: 25 July 2026 | Viewed by 695

Special Issue Editor

Dr. Xingjian Chen

E-Mail Website
Guest Editor
MGH & Harvard Medical School, Harvard University, Boston, MA, USA
Interests: sustainable modeling; deep learning; modelling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent breakthroughs in high-throughput sequencing, multi-omics technologies, and imaging have produced increasingly large and complex biological datasets. These developments have elevated the role of bioinformatics and computational biology in advancing life sciences research. From uncovering molecular mechanisms to enabling precision medicine, computational methods are now indispensable tools for interpreting biological complexity.

This Special Issue aims to highlight innovative computational approaches, analytical techniques, and modelling frameworks that address current challenges in biology and biomedicine. We particularly welcome interdisciplinary studies that integrate bioinformatics with experimental validation, as well as those that contribute to sustainable and reproducible research practices.

By bringing together contributions from computer science, mathematics, biology, and medicine, this Special Issue will serve as a platform to foster cross-disciplinary collaboration and methodological innovation in the field.

Suggested themes. We invite submissions related to, but not limited to, the following themes:

  • Computational modelling and simulation in biological systems.
  • Machine learning and artificial intelligence for omics data.
  • Big data analytics for sustainable development in life sciences.
  • Multi-modal and integrative analysis of heterogeneous biological datasets.
  • Bioinformatics tools and pipelines for high-throughput data.
  • Spatial transcriptomics and single-cell data interpretation.
  • Evaluation and benchmarking of computational models.

In this Special Issue, original research articles and reviews are welcomed.

Dr. Xingjian Chen
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 250 words) can be sent to the Editorial Office for assessment.

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

  • bioinformatics
  • computational biology
  • machine learning in biomedicine
  • artificial intelligence
  • single-cell and spatial transcriptomics analysis
  • biological network analysis
  • high-throughput data analysis

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

Published Papers (1 paper)

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Research

17 pages, 1223 KB  
Article
A Deep Autoencoder Compression-Based Genomic Prediction Method for Whole-Genome Sequencing Data
by Hailiang Song, Tian Dong, Wei Wang, Xiaoyu Yan, Chenfan Geng, Song Bai and Hongxia Hu
Biology 2025, 14(11), 1622; https://doi.org/10.3390/biology14111622 - 19 Nov 2025
Viewed by 334
Abstract
Genomic prediction using whole-genome sequencing (WGS) data is challenged by the imbalance between a limited sample size (n) and an extensive number of single-nucleotide polymorphisms (SNPs) (p), where n ≪p. The high dimensionality of WGS data [...] Read more.
Genomic prediction using whole-genome sequencing (WGS) data is challenged by the imbalance between a limited sample size (n) and an extensive number of single-nucleotide polymorphisms (SNPs) (p), where n ≪p. The high dimensionality of WGS data also increases computational demands, limiting its practical application. In this study, we introduce DAGP, a novel method that integrates deep autoencoder compression to reduce WGS data dimensionality by over 99% while preserving essential genetic information. This compression significantly improves computational efficiency, facilitating the effective use of high-dimensional genomic data. Our results demonstrated that DAGP, when combined with the genomic best linear unbiased prediction (GBLUP) method, maintained prediction accuracy comparable to WGS data, even at reduced marker densities of 50 K for sturgeon and 20 K for maize. Furthermore, integrating DAGP with Bayesian and machine learning models improved genomic prediction accuracy over traditional WGS-based GBLUP, with an average gain of 6.05% and 5.35%, respectively. DAGP provides an efficient and scalable solution for genomic prediction in species with large-scale genomic data, offering both computational feasibility and enhanced prediction performance. Full article
(This article belongs to the Special Issue Methods in Bioinformatics and Computational Biology)
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