Computational Molecular Biology 2024, Vol.14, No.1, 28-35 http://bioscipublisher.com/index.php/cmb 32 assessment of diseases. In early diagnosis, network biology integrates multi-omics data to comprehensively analyze changes in biomolecular networks, thereby identifying early biological markers related to diseases. Samme et al. (2019) found that these biomarkers may include changes in the expression of specific genes and abnormal patterns in protein interaction networks, providing early warning signals before the onset of disease symptoms. By constructing disease-related biomolecular network models, network biology can identify these early biomarkers and develop corresponding detection methods, providing strong support for the early detection of diseases. In prognosis assessment, new strategies in network biology also play a crucial role. Prognosis assessment is key to determining the trend of disease progression and patient survival expectations. Network biology analyzes the dynamic changes in biomolecular networks to predict the rate of disease progression and patient prognosis. For example, by analyzing the gene expression profiles and protein interaction networks of tumor cells, network biology can reveal the molecular mechanisms of malignant behaviors such as proliferation, migration, and invasion of tumor cells, and thus predict the malignancy of the tumor and the patient's survival period. These predictions can guide doctors in formulating personalized treatment plans and providing prognosis guidance, helping patients better manage their diseases (Jiang et al., 2020). New strategies in network biology also have high sensitivity and specificity, reducing the incidence of misdiagnosis and missed diagnosis. By integrating multi-omics data and network analysis methods, network biology can comprehensively consider multiple biomarkers and multiple layers of information, improving the accuracy and reliability of diagnoses. This multidimensional analysis approach can more comprehensively assess a patient's disease state, providing a more reliable basis for early diagnosis and prognosis assessment. 3.2 The potential of new network biology strategies in personalized treatment and precision medicine In personalized treatment, network biology by constructing and analyzing biomolecular networks, can reveal the molecular differences between individuals. These differences include changes in gene expression, protein interactions, and metabolic pathways, which directly affect an individual's disease onset, progression, and drug response. New strategies in network biology can tailor treatment plans for each patient based on individual biomolecular network characteristics, selecting the most appropriate drugs, dosages, and treatment timings. This personalized treatment approach helps enhance treatment effectiveness, reduce unnecessary side effects, and improve patients' quality of life (Chiva et al., 2018). Precision medicine emphasizes using individualized information and biomarkers for precise decision-making in disease diagnosis, prevention, and treatment. New network biology strategies provide strong support for precision medicine. David et al. (2020) analyzed individual biomolecular networks and identified specific genes, proteins, and metabolites related to diseases. These biomarkers are not only useful for early disease diagnosis but also for predicting disease progression and prognosis, guiding precise treatment. Additionally, network biology strategies can assess individual drug responsiveness and resistance, providing decision-making basis for precision medication. By adopting an integrated and collaborative approach, David et al. (2020) identified conservative mechanisms in three pathogenic coronavirus strains and further researched potential drug targets. This versatile approach is broadly applicable to other pathogens and disease areas. The application of new network biology strategies in personalized treatment and precision medicine is expected to drive a shift in the medical paradigm. Traditional medical models often base disease diagnosis and treatment on group averages, overlooking individual differences. Network biology strategies can fully consider individual biomolecular network characteristics, achieving truly personalized medicine (Figure 2). This will help improve medical outcomes, reduce medical costs, and promote the sustainable development of the healthcare industry. 3.3 Practical applications of new network biology strategies in drug development and optimization 3.3.1 Target identification and validation In cancer drug development, network biology is used to analyze the gene and protein interaction networks within cancer cells. By analyzing abnormally active signaling pathways in cancer cells, researchers can identify key
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