Computational Molecular Biology 2024, Vol.14, No.1, 28-35 http://bioscipublisher.com/index.php/cmb 31 Proteomics data integration occupies a crucial role in network biology. Techniques like mass spectrometry and immunoprecipitation help acquire information about protein expression levels, modification states, and interaction relationships. Network biology combines these proteomics data with genomic and transcriptomic data to build protein interaction networks and analyze changes in protein functions in diseases (Figure 1). This cross-omics integration method enables a more comprehensive understanding of the roles proteins play in diseases, providing new insights for the diagnosis and treatment of diseases. 2.2 The application of network biology in identifying key disease proteins and predicting drug targets Network biology plays a critical role in identifying key disease proteins and predicting drug targets. In identifying key disease proteins, network biology constructs protein interaction networks and can systematically analyze the interactions between proteins, thereby revealing their crucial roles in the onset and progression of diseases. Using bioinformatics algorithms and large-scale datasets, network biology can identify proteins that exhibit abnormal expression or interactions in a diseased state, which are often key molecules in disease onset (Qin et al., 2020). In predicting drug targets, network biology also shows great potential. Drug targets are specific molecules or structures within a biological system that drugs act upon, which are crucial for the efficacy and safety of the drugs. Network biology, by analyzing protein interaction networks and gene regulatory networks, can predict potential drug targets. For example, by comparing the protein interaction networks of normal and diseased cells, it can identify proteins that show abnormal interactions in a diseased state, which may be effective targets for drug intervention. Additionally, network biology can also analyze gene expression profiles and transcription regulatory networks, predicting genes and transcription factors closely related to disease onset, thus providing new candidate molecules for drug design. The application of network biology in identifying key disease proteins and predicting drug targets helps to deepen understanding of disease pathogenesis and drug action mechanisms, providing new strategies and tools for precise treatment and new drug development. Combining multi-omics data and network analysis methods allows for more accurate identification of key disease proteins and drug targets, supporting early diagnosis, personalized treatment, and drug development for diseases. 2.3 The important role of network biology in the mechanisms of disease onset and progression Network biology, by constructing and analyzing biological molecular networks, provides a global and systemic perspective to examine diseases. It integrates genomics, transcriptomics, proteomics, and other multi-omics data, forming multi-level, multidimensional information networks that enable a deeper understanding of the interactions and regulatory relationships between biomolecules. This global analysis method overcomes the limitations of traditional single-molecule studies and can reveal the complexity and diversity of disease onset and progression. Network biology can reveal key molecules and pathways during the disease development process. Research by Lokau et al. (2020) found that by analyzing the topological structure and dynamics of biological molecular networks, network biology can identify nodes and edges that show abnormal changes in a diseased state, i.e., key molecules and interactions. These key molecules may be critical drivers of disease onset and could also be potential targets for treatment. Network biology can also predict disease progression trends and individual differences. By analyzing the dynamic changes in biological molecular networks under different times and conditions, network biology can predict the disease trajectory and outcome. Combining an individual's genetic background and environmental factors, network biology can also perform personalized disease prediction and risk assessment. This predictive capability can help identify potential disease risks in advance, develop personalized treatment plans, and improve the accuracy and effectiveness of disease diagnosis and treatment. 3 Applications of New Network Biology Strategies in Disease Diagnosis and Treatment 3.1 Application of new network biology strategies in early diagnosis and prognosis assessment of diseases New strategies in network biology demonstrate tremendous potential in the early diagnosis and prognosis
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