Computational Molecular Biology 2024, Vol.14, No.1, 28-35 http://bioscipublisher.com/index.php/cmb 34 responses and resistance to specific drugs. This helps doctors tailor personalized medication plans for patients, enhancing treatment effectiveness and reducing unnecessary side effects (Goddard et al., 2018). These practical applications demonstrate the significant role of new network biology strategies in drug development and optimization. As technology advances and research deepens, these strategies are expected to bring more innovations and breakthroughs to the drug development field, contributing greatly to human health. 4 Summary and Outlook As bioinformatics and computational biology rapidly develop, network biology has emerged as an interdisciplinary field revealing the complex networks between protein functions and diseases. Network biology integrates multi-omics data, including genomics, transcriptomics, and proteomics, and constructs interaction networks among biomolecules. It offers a global and systemic perspective to deeply analyze the mechanisms of proteins in biological activities. This review aims to summarize the crucial role of network biology in unveiling the relationships between protein functions and diseases and to highlight its new strategies in the practical applications and potential in disease diagnosis and treatment. Network biology plays a key role in revealing the relationship between protein functions and diseases. By building protein interaction networks, it can systematically analyze the interactions among proteins, thereby identifying those crucial in the onset and progression of diseases. For example, network biology can identify proteins that are abnormally expressed or interact abnormally with other proteins under disease conditions, which are often key molecules in disease onset. Additionally, network biology can analyze the redundancy and complementarity of protein functions, further deepening our understanding of the complexity of protein functions. These research findings not only help understand the pathological mechanisms of diseases but also provide new ideas and methods for disease diagnosis and treatment. The practical applications and potential of new strategies in network biology in disease diagnosis and treatment are increasingly evident. In diagnostics, network biology can construct disease-related biomolecular network models, predicting the risk and progression speed of diseases. Analyzing individual biomolecular network characteristics can enable early diagnosis and precise classification of diseases, providing personalized treatment plans for patients. In therapeutics, network biology can predict the efficacy and side effects of drugs, guiding the research and optimization of drugs. Identifying key drug targets can lead to the design of more precise and effective drugs, improving treatment outcomes and reducing the occurrence of side effects. The future of network biology in revealing the relationship between protein functions and diseases holds immense research potential. With the continuous development of high-throughput sequencing technologies, obtaining more accurate biomolecular data will provide a more solid foundation for research in network biology. As artificial intelligence and machine learning technologies advance, more intelligent and efficient network analysis methods will be developed, enhancing the analytical capabilities and predictive accuracy of network biology. Conflict of Interest Disclosure The authors affirm that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. References Akdel M., Durairaj J., de Ridder D., van Dijk A.D., and Caretta J., 2020, A multiple protein structure alignment and feature extraction suite, Comput. Struct. Biotechnol. J., 18: 981-992. https://doi.org/10.1016/j.csbj.2020.03.011 Becerra-Flores M., and CardozoT., 2020, SARS-CoV-2 viral spike G614 mutation exhibits higher case fatality rate, Int. J. Clin. Pract., 74: e13525. https://doi.org/10.1111/ijcp.13525 Chiva C., Olivella R., Borràs E., Espadas G., Pastor O., Solé A., and Sabidó E., 2018, QCloud: A cloud-based quality control system for mass spectrometry-based proteomics laboratories, PLOS ONE, 13: e0189209. https://doi.org/10.1371/journal.pone.0189209 David E.G., Joseph H., Mehdi B., Veronica V.R., Svenja U., Hannes B., Alexander S.J., Kirsten O., Jeffrey Z.G., and Nevan J.K., 2020, Comparative host-coronavirus protein interaction networks reveal pan-viral disease mechanisms, Science, 370: 6521.
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