Artificial intelligence independently overcomes 120 years of unsolved mystery in the biological field
For the first time in history, the computer did not rely on any human help, and independently discovered a new scientific theory through artificial intelligence. Scientists in the computational and biological fields from Tufts University have coded a program that allows computers to independently develop a theory that explains the problem in the face of a new scientific problem. They chose a phenomenon that puzzled scientists for 120 years in the biological field: After being cut, Planaria was able to regenerate and form a new individual. This strange phenomenon was discovered long time ago, but as for why it happened, there is no clear answer yet. However, when this problem is handed over to the computer, it can be reverse engineered to find a theory that can explain the mechanism behind the process. Specific details and methods of artificial intelligence were published in the recent PLOS Computational Biology. The researchers said that what they really want to discover is not how a new organ is regenerated, but how the body guides the new organ to the desired appearance and size. These mysterious messages are hidden in the genes. "Now, most of the regenerative models obtained from genetic experiments are charts filled with arrows, telling you that this gene regulates that gene, which of course is fine, but it does not tell you what the organ will eventually develop. You can't tell from these genetic regulatory pathway models whether the body will eventually grow into a tree, an octopus, or eventually become human," said Levin, one of the researchers. “Those models just tell you which ingredients are essential when regeneration occurs, but it doesn’t show you how this regeneration process takes place step by step.†Therefore, researchers want to use algorithms to create a new regenerative model that tells us how the stimulation of a given organism can lead to the regeneration of a particular component. In order to solve this problem, the researchers used the idea of ​​evolutionary computing to develop an algorithm that can achieve "self-evolution" and accurately predict the research data obtained by researchers in the planarization experiment. They hope to find a regulatory network that can interpret all published experimental data through this algorithm. So how is this regulation network specifically found? Perhaps you have already thought that in the beginning, the "un-evolved" randomly generated regulatory network would generally not produce any the same results as the real experiment. The new candidate control network is generated by combining the previously existing regulatory networks, and on this basis, random deletions are added. All candidate networks are tested in a simulation test, and the algorithm compares the results developed by this candidate regulatory network with the actual experimental data in the database. In this "evolution" process, new candidate regulatory networks that can interpret more experimental data are gradually being produced. The researchers finally applied this algorithm to a database of 16 key experimental data regenerated by the planarian, hoping to find a regulatory network that could fully explain the regeneration of the planarian. After 42 hours, the algorithm gave a regulatory network model that accurately simulated all experimental data from 16 key experiments. This new regulatory network contains important regulatory molecules known as well as two undiscovered proteins. For such an inspiring experimental result, the researchers said: "The biggest significance of this project is that we are not an unusually complex and incomprehensible regulatory network. On the contrary, the results show that artificial intelligence can help us discover A simple, easy-to-understand model. This means that artificial intelligence can penetrate any field of modern research, not just data mining of big data generated in experiments, but also help us understand the behind the complex data. The principle of simplicity." Our company is mainly engaged in the production of NATURAL PLANT EXTRACT raw materials, and is a famous supplier of plant extract raw materials in China.
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