Computational Molecular Biology 2025, Vol.15, No.3, 131-140 http://bioscipublisher.com/index.php/cmb 137 models "speak human language", such as using simpler algorithms or adding interpretation tools like SHAP, allowing people to see exactly how much effect each feature plays (Zeng et al., 2024). Even so, to truly enter clinical practice, several hurdles still need to be overcome - the model must be verified in clinical trials, and the detection of markers cannot be too complicated (Baek et al., 2024). Otherwise, even the smartest model may only remain at the research stage. 7.3 Data sharing, privacy protection and ethical issues The genomic and clinical data of patients are indeed a "gold mine" for scientific research, but problems also arise - privacy, ethics, and regulation - none of which can be avoided. Regulations in all countries are very strict, and it is almost impossible to freely use these data. Some people have proposed controlled sharing, while others have attempted federated learning, hoping to strike a balance between "opening" and "preventing" (Calvino et al., 2024; Chandrashekar et al., 2024). What researchers can do is to advance their work as much as possible under the premise of adhering to the ethical bottom line, making the data valuable while also ensuring that patients' rights and interests are not infringed upon. 8 Future Outlook and Conclusions Looking ahead, the weight of artificial intelligence in drug response prediction will only become increasingly significant. The more data there is and the stronger the computing power, the more detailed and accurate the model will be trained. However, nowadays people no longer merely pursue "accuracy", but also want models to explain "why" clearly. Thus, the integration of interpretability and biological knowledge has become a new direction. On the other hand, multimodal fusion is also on the rise - not only looking at genetic or transcriptional information, but also taking into account the patient's clinical indicators and imaging data to piece together a more complete "digital patient". This integration approach might enable AI to truly understand individual differences, thereby being closer to reality when predicting drug responses. When these technologies mature, perhaps precise medication will no longer be the goal but will become a habit. In the future medical scene, it is likely that it will not be as simple as just looking at medical records and images. The patient information in the hands of doctors may also carry a complete set of genomic sequencing results, transcriptional profiling data, and even real-time monitored molecular changes. As hospitals accumulate more and more such comprehensive data, the boundary between clinical practice and omics will become blurred. Imagine a system that can simultaneously understand molecular-level features and, in combination with the patient's clinical condition, judge drug responses. This "clinical-omics integration" decision-making approach is much more precise than the past empirical speculation. The emergence of liquid biopsy has also made dynamic adjustment treatment a reality. In the future, multi-center collaboration and data sharing may make these systems smarter and smarter as they are used. At that time, doctors did not rely solely on intuition but had a complete set of intelligent tools behind them to support them. The most fundamental goal of precision medicine is to ensure that the right medicine is used for the right people. Biomarker screening serves precisely this purpose - it can inform doctors in advance who is more likely to benefit and who is at greater risk. EGFR mutations in lung cancer are a typical example, transforming targeted therapy from an attempt into a precise strike. For pharmaceutical companies, such markers are equally crucial. New drug trials no longer involve blind submissions but first identify potentially sensitive patient groups to increase success rates and shorten approval cycles. The drug resistance mechanisms revealed by multi-omics studies are also constantly providing new directions for new drugs. For instance, osimertinib was developed to address the drug resistance of EGFR T790M mutations. In the future, biomarkers will no longer be regarded as "add-ons" in new drug development, but will be incorporated into the design from the very beginning. When drugs and biomarkers appear in pairs, the development efficiency will naturally be higher. Acknowledgments We would like to express my heartfelt thanks to all the teachers who have provided guidance for this study.
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