Network-Based Machine Learning Approaches in Bioinformatics
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Entropy and Biology".
Deadline for manuscript submissions: closed (8 February 2026) | Viewed by 2472
Special Issue Editor
Interests: bioinformatics; computational biology; systems biomedicine; machine learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The exploration of intricate biological networks has emerged as a novel paradigm for elucidating molecular functions on a systematic scale. The endeavor to model and dissect the intrinsic, dynamic, and structural attributes of these networks from a topological standpoint stands as a pivotal concern in contemporary bioinformatics and biomedical informatics research. However, this endeavor is fraught with challenges stemming from the vast scope and intricate connectivity of biological networks, underpinned by massive datasets. Notable instances encompass metabolic networks, gene regulatory networks (GRNs), RNA interaction networks, protein–protein interactions (PPIs), and their myriad intersections with drugs and diseases.
In the realm of bioinformatics, a diverse array of graph-theoretic computational methodologies has been deployed to facilitate the meticulous analysis of these large-scale, multifaceted biological networks. Furthermore, the integration of these approaches with other big data modalities promises to bolster the robustness of network modeling and enhance the precision of network analysis. Such endeavors hold immense promise for diverse biomedical applications, including precision medicine and drug repositioning.
This Special Issue serves as a theme of collection for discussing the cutting-edge methodologies pertaining to biological and biomedical network analysis. It lends special emphasis to network-based machine learning approaches, which harness the interconnectedness of biological entities to derive insights into their functions and interactions. This Special Issue will delve into the nuances of these approaches, emphasizing their application in unraveling the complexities of biomolecular networks. Furthermore, it will underscore the importance of comparing the efficacy of different methodologies in addressing these biological queries.
The topics covered in this Special Issue are as follows:
- Network-based machine learning for biological systems;
- Entropy-based network analysis;
- Gene regulatory network (GRN) analysis using network-based learning;
- RNA interaction network;
- Protein–protein interaction (PPI) network;
- Network modeling and link prediction;
- Function prediction in biological networks;
- Pathway discovery through network analysis;
- Network dynamics and evolution;
- Graph data mining algorithms in bioinformatics;
- Network biology for complex diseases;
- Biomedical applications of network analysis.
This Special Issue aims to foster a comprehensive understanding of network-based machine learning approaches in bioinformatics, promoting the exchange of ideas, methodologies, and applications across the scientific community. It invites contributions from researchers and practitioners working at the forefront of this exciting and rapidly evolving field.
Prof. Dr. Zhi-Ping Liu
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. Entropy 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
- networks
- machine learning
- bioinformatics
- systems biomedicine
- deep learning
- graph neural networks
- biomolecular interactions
- biomarker discovery
- context-specific pathways in diseases
Benefits of Publishing in a Special Issue
- Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
- Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
- Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
- External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
- Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.
Further information on MDPI's Special Issue policies can be found here.
