Computational Molecular Biology 2025, Vol.15, No.4, 171-182 http://bioscipublisher.com/index.php/cmb 17 9 rules. Sometimes the model can be interpreted as "A is connected to B via X and Y", or the most critical local structures can be marked. Such results are more acceptable. The ideal system in the future should be able to provide reasoning basis like a human being, rather than merely throwing out a score (Rajabi et al., 2022). On the other hand, the issue of uncertainty is also tricky. Biological knowledge has never been black and white. Some interactions only hold true in specific cells, and some conclusions remain at the hypothesis stage. If a graph is described entirely by definite relationships, it will instead be distorted. A more realistic approach is to score each edge and indicate the confidence level. For instance, 0.8 represents high support, while 0.3 indicates insufficient evidence. In this way, when doctors see the prediction that "drug X may act on disease Y", they can also have a clear idea. One more point needs to be reminded: The incompleteness of the map does not mean "there is no such relationship", but just "it has not yet been discovered". It would be best for future systems to present such a grayscale, for instance, allowing users to see confidence intervals, literature differences, and even providing a "counterexample" button to help people understand the boundaries of knowledge (Bahaj et al., 2024). Science has always advanced in uncertainty, and knowledge graphs should also learn to recognize this. 7.3 Future development direction The molecular interaction knowledge graph is just the starting point; there are simply too many directions for future development. For instance, multimodal fusion - most of the current graphs only handle structured data, which seems a bit "thin". If the molecular structure, cell images and even the content of the literature could all be integrated, the information would be much more three-dimensional. Imagine that after the protein-protein interaction network is combined with the three-dimensional structure, researchers can directly see where the interaction interface is. For instance, intelligent question answering. In the future, researchers may not need to go through databases. They just need to ask, "Which proteins are involved in insulin signaling and are related to Alzheimer's disease?" The system can answer and also provide the literature path. The scene where a doctor asks about the patient's condition and the system makes a diagnosis is not far from reality. The collaboration of knowledge graphs is also a trend - relying on a few database teams for updates is clearly insufficient. In the future, perhaps everyone will be able to upload new discoveries, allowing knowledge graphs to grow and roll like Wikipedia. Of course, to ensure quality, audits and standards are indispensable. There are also more cutting-edge directions such as cross-species and spatio-temporal maps, which not only allow for the observation of differences but also the tracking of dynamic changes in life processes (López et al., 2024). Even the graph can guide experiments in reverse, with machines proposing hypotheses, automatically verifying them, and then providing feedback for updates. By then, scientific research and Atlantis may have merged into one. In other words, this technology is just getting started, and its future is more wonderful than we can imagine now. 8 Conclusion This research is essentially answering a question: How to organize the scattered information on molecular interactions into a knowledge system that can be understood by computers and directly used by researchers. Let's start with the most basic data. First, process the biological information from various sources, unify the names, relationships and formats, so that they can make sense on the same picture. Next comes the modeling process, which involves determining which nodes to count, which relationships to count, and what kind of structure to use to accommodate these elements. After the graph was set up, we also implemented an automated process to make the construction process more like an assembly line, allowing for repeated execution. The analysis part is more like mining: using embedding learning methods to convert the graph into vectors that machines can calculate, and then identifying key molecules and potential functional modules through metrics such as centrality and community. We chose protein-protein interactions as a case study, and the results were quite interesting. Besides reproducing the known relationships, we also unearthed some new interactions, and even had experimental support. Finally, we also developed a prototype system that can be both checked and viewed. Overall, this framework has successfully completed the process of "construction - analysis - verification - application", laying a solid foundation for more complex graph research in the future. This research is not merely a matter of technology stacking. First, we brought the idea of knowledge graphs into the field of molecular interactions, attempting to use it to integrate biological data that "speak different languages"
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