Next-Generation Computational Approaches for Biological Network Analysis
Protein-protein interaction (PPI) networks are critical to understanding cellular processes and disease mechanisms. Computational advances have transformed PPI analysis, allowing for the prediction, analysis, and visualization of intricate interaction networks. This article discusses the basics of P...
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| Format: | Article |
| Language: | English |
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QAASPA Publisher
2025-06-01
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| Series: | BioMed Target Journal |
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| Online Access: | https://qaaspa.com/index.php/bmtj/article/view/70 |
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| author | Hamza Ali Mari Maham Taqi Abrar Ahmed Rattar Ahsan Jamal Memon Muhammad Talha Nasir Arleen Yousuf |
| author_facet | Hamza Ali Mari Maham Taqi Abrar Ahmed Rattar Ahsan Jamal Memon Muhammad Talha Nasir Arleen Yousuf |
| author_sort | Hamza Ali Mari |
| collection | DOAJ |
| description | Protein-protein interaction (PPI) networks are critical to understanding cellular processes and disease mechanisms. Computational advances have transformed PPI analysis, allowing for the prediction, analysis, and visualization of intricate interaction networks. This article discusses the basics of PPI networks, experimental and computational methods for their detection and analysis, and novel predictive models. We cover sequence-based approaches, such as homology, domain, and motif-based methods, as well as structure-based methods like structural alignment, comparison, and interface-based prediction. Functional annotation-based methods, such as Gene Ontology (GO) annotations, pathway-based methods, and co-expression data, are also discussed. Machine learning methods, such as supervised and unsupervised models, neural networks, and deep learning, increasingly contribute to improving PPI predictions. In addition, network inference methods, including Bayesian networks, graph-based approaches, and integrative multi-omics strategies, extend our understanding of biological systems. Key applications of PPI networks are the prioritization of disease genes, annotating uncharacterized proteins' functions, analyzing pathways, and discovering biomarkers. Yet, incompleteness and noisiness of data, false positives and negatives, and scalability limitations of computational methods continue to pose problems. Emerging directions highlight upcoming technologies, advances in machine learning, and multi-omics integration with the potential for steering personalized medicine and precision health. |
| format | Article |
| id | doaj-art-18163a19e08b47a2be00a7fb8091a7f5 |
| institution | Matheson Library |
| issn | 2960-1428 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | QAASPA Publisher |
| record_format | Article |
| series | BioMed Target Journal |
| spelling | doaj-art-18163a19e08b47a2be00a7fb8091a7f52025-07-14T15:06:15ZengQAASPA PublisherBioMed Target Journal2960-14282025-06-0131324810.59786/bmtj.31370Next-Generation Computational Approaches for Biological Network AnalysisHamza Ali Mari0https://orcid.org/0000-0001-9993-9277Maham Taqi1https://orcid.org/0009-0009-2547-9066Abrar Ahmed Rattar2https://orcid.org/0000-0001-8975-3591Ahsan Jamal Memon3https://orcid.org/0000-0001-7989-311XMuhammad Talha Nasir4https://orcid.org/0009-0001-7988-1191Arleen Yousuf5https://orcid.org/0009-0001-1374-8072 Medical Research Centre, Liaquat University of Medical and Health Sciences (LUMHS), Jamshoro 76080, PakistanDepartment of Molecular Biology, Liaquat University of Medical and Health Sciences (LUMHS), Jamshoro, 76080, PakistanDepartment of Medicine, Liaquat University of Medical and Health Sciences (LUMHS), Jamshoro, 76080, PakistanDepartment of Medicine, Liaquat University of Medical and Health Sciences (LUMHS), Jamshoro, 76080, PakistanMedical Research Centre, Liaquat University of Medical and Health Sciences (LUMHS), Jamshoro, 76080, PakistanInstitute of Biotechnology and Genetic Engineering, University of Sindh, Jamshoro 76080, PakistanProtein-protein interaction (PPI) networks are critical to understanding cellular processes and disease mechanisms. Computational advances have transformed PPI analysis, allowing for the prediction, analysis, and visualization of intricate interaction networks. This article discusses the basics of PPI networks, experimental and computational methods for their detection and analysis, and novel predictive models. We cover sequence-based approaches, such as homology, domain, and motif-based methods, as well as structure-based methods like structural alignment, comparison, and interface-based prediction. Functional annotation-based methods, such as Gene Ontology (GO) annotations, pathway-based methods, and co-expression data, are also discussed. Machine learning methods, such as supervised and unsupervised models, neural networks, and deep learning, increasingly contribute to improving PPI predictions. In addition, network inference methods, including Bayesian networks, graph-based approaches, and integrative multi-omics strategies, extend our understanding of biological systems. Key applications of PPI networks are the prioritization of disease genes, annotating uncharacterized proteins' functions, analyzing pathways, and discovering biomarkers. Yet, incompleteness and noisiness of data, false positives and negatives, and scalability limitations of computational methods continue to pose problems. Emerging directions highlight upcoming technologies, advances in machine learning, and multi-omics integration with the potential for steering personalized medicine and precision health.https://qaaspa.com/index.php/bmtj/article/view/70protein-protein interaction networkscellular functionsbiological processescomputational techniquesmodeling and analysisdrug discovery |
| spellingShingle | Hamza Ali Mari Maham Taqi Abrar Ahmed Rattar Ahsan Jamal Memon Muhammad Talha Nasir Arleen Yousuf Next-Generation Computational Approaches for Biological Network Analysis BioMed Target Journal protein-protein interaction networks cellular functions biological processes computational techniques modeling and analysis drug discovery |
| title | Next-Generation Computational Approaches for Biological Network Analysis |
| title_full | Next-Generation Computational Approaches for Biological Network Analysis |
| title_fullStr | Next-Generation Computational Approaches for Biological Network Analysis |
| title_full_unstemmed | Next-Generation Computational Approaches for Biological Network Analysis |
| title_short | Next-Generation Computational Approaches for Biological Network Analysis |
| title_sort | next generation computational approaches for biological network analysis |
| topic | protein-protein interaction networks cellular functions biological processes computational techniques modeling and analysis drug discovery |
| url | https://qaaspa.com/index.php/bmtj/article/view/70 |
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