Advancing Translational Science Using Bioinformatics and Big Data-Driven Approaches

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

Deadline for manuscript submissions: 30 April 2026 | Viewed by 1196

Special Issue Editor


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Guest Editor
Department of Health Outcomes Research and Policy, Harrison College of Pharmacy, Auburn University, Auburn, AL 36849, USA
Interests: biological data mining; systems biology; network biology; artificial intelligence; visual analytics; translational bioinformatics

Special Issue Information

Dear Colleagues,

This Special Issue of MDPI Biology focuses on accelerating progress in translational science through the integration of bioinformatics and big data analytics. The rapid evolution of artificial intelligence (AI) has dramatically enhanced the potential of biological discovery by empowering bioinformatics tools with greater accuracy, scalability, and predictive power. Meanwhile, biomedical research has been transformed by multi-modal technologies that generate high-dimensional datasets across diverse biological systems.

Together, these developments form a powerful synergy: AI-driven bioinformatics and big data approaches are redefining how we predict, diagnose, and treat disease, laying the foundation for next-generation personalized and precision medicine.

The goal of this Special Issue is to inspire researchers to explore computational strategies and uncover novel insights into disease mechanisms, onset, and progression. We particularly welcome submissions that present new tools, methodologies, or applications leveraging bioinformatics and big data to address challenges in translational science. This issue will also cover application in areas enabled by integrative analyses, including but not limited to medical pathology, biomarker discovery, ethical and safety considerations, functional genomics analysis, and the impact of AI-driven computational solutions.

This Special Issue will foster interdisciplinary collaboration and advance translational research, ultimately improving clinical outcomes through data-informed discovery.

Dr. Zongliang Yue
Guest Editor

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Keywords

  • bioinformatics
  • big data analytics
  • artificial intelligence (AI)
  • machine learning
  • deep learning
  • translational science
  • computational biology
  • drug repositioning
  • biomarker discovery
  • genomics
  • medical imaging
  • digital pathology
  • integrative analysis
  • disease mechanisms
  • functional genomics analysis
  • computational tool application
  • ethical and safety considerations
  • data-driven approaches

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Published Papers (1 paper)

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Research

15 pages, 6405 KiB  
Article
Integrative Single-Cell Transcriptomics and Network Modeling Reveal Modular Regulators of Sheep Zygotic Genome Activation
by Xiaopeng Li, Peng Niu, Kai Hu, Xueyan Wang, Fei Huang, Pengyan Song, Qinghua Gao and Di Fang
Biology 2025, 14(6), 676; https://doi.org/10.3390/biology14060676 - 11 Jun 2025
Viewed by 782
Abstract
Zygotic genome activation (ZGA) marks the critical transition from reliance on maternal transcripts to the initiation of embryonic transcription early in development. Despite extensive characterization in model species, the regulatory framework of ZGA in sheep remains poorly defined. Here, we applied single-cell RNA [...] Read more.
Zygotic genome activation (ZGA) marks the critical transition from reliance on maternal transcripts to the initiation of embryonic transcription early in development. Despite extensive characterization in model species, the regulatory framework of ZGA in sheep remains poorly defined. Here, we applied single-cell RNA sequencing (Smart-seq2) to in vivo- and in vitro-derived sheep embryos at the 8-, 16-, and 32-cell stages. Differential expression analysis revealed 114, 1628, and 1465 genes altered in the 8- vs. 16-, 16- vs. 32-, and 8- vs. 32-cell transitions, respectively, with the core pluripotency factors SOX2, NANOG, POU5F1, and KLF4 upregulated during major ZGA. To uncover coordinated regulatory modules, we constructed a weighted gene co-expression network using WGCNA, identifying the MEred module as most tightly correlated with developmental progression (r = 0.48, p = 8.6 × 10−14). The integration of MERed genes into the STRING v11 protein–protein interaction network furnished a high-confidence scaffold for community detection. Louvain partitioning delineated two discrete communities: Community 0 was enriched in ER–Golgi vesicle-mediated transport, transmembrane transport, and cytoskeletal dynamics, suggesting roles in membrane protein processing, secretion, and early signaling; Community 1 was enriched in G2/M cell-cycle transition and RNA splicing/processing, indicating a coordinated network for accurate post-ZGA cell division and transcript maturation. Together, these integrated analyses reveal a modular regulatory architecture underlying sheep ZGA and provide a framework for dissecting early embryonic development in this species. Full article
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