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Purpose-As of March 2020, this paper collected 41 prescriptions for Covid-19 in official reports, and used machine learning and knowledge graph technology to discover the rules of common prescription medication for Covid-19 and the Chinese medicinal herb groups applicable to different groups of people to help the TCM prescriptions for the clinical treatment of Covid-19. Methods-The FP-Growth algorithm was used to analyze the properties, tastes, and meridian tropism of Chinese medicinal herb groups in prescription data, constructing a knowledge graph for Covid-19 description and mining the prescription medication rules.Results-In the 41 pieces of prescription, patients in medical observation period were treated with Chinese medicinal herbs that can relieve exterior syndromes and stop vomiting, such as Radix Saposhnikoviae, Semen Sojae Preparatum, etc. Those with mild common syndromes were treated with heat-clearing and detoxicating Chinese medicinal herbs, such as Rhizoma Coptidis, Radix Scutellariae, etc. Infected patients were treated with Chinese medicinal herbs such as Radix Glycyrrhizae, Herba Asari, etc., which can dissolve phlegm, relieve cough and invigorate the spleen, relieve pain, expel wind syndromes, relieve exterior symptoms and help digesting. Conclusion-Combining machine learning and knowledge graph technology to analyze the data characteristics of common prescriptions used by different populations can help analyze the core medication mechanism of TCM (Traditional Chinese Medicine) treatment of different populations with the same disease.

The essential proteins in protein networks play an important role in complex cellular functions and their evolution. Therefore, searching essential proteins in protein networks can help to explain the structure, function and dynamics of basic cellular networks. The existing dynamic protein network regards the protein situation at all times as the same, but in fact, the role of proteins varies at different times. In order to improve the accuracy of essential protein searching, an improved H-index algorithm based on attenuation coefficient method is proposed in this paper, which incorporates the neglected node information into consideration to improve the accuracy of essential protein searching. The experiments show that the essential proteins found on the basis of this model are more effective than other similar methods.

Traditional Chinese medicine is a treasure house of the Chinese nation. Using data mining method to study traditional Chinese medicine can summarize the experience of traditional Chinese medicine and promote the development of traditional Chinese medicine. The dose-effect relationship of traditional Chinese medicine mainly refers to that the effect of drugs changes with the dose of drugs in a certain range, and there is a great relationship between the effect produced by different combinations of drugs and the dose of drugs. With the development of data mining technology, it is widely used to study the dose-response relationship between drugs with the data of famous old traditional Chinese medicine doctor's medical record. In this paper, based on the characteristics of traditional Chinese doctor's medicine data, the Apriori algorithm is improved from the construction of the data model, so that the transaction database does not need to be converted into Boolean database in the mining, which can improve the reseracher's friendliness. The results of the excavation not only included the common drugs used by famous tra-dional Chinese medicine doctors to treat lung cancer, but also analyzed the proportional relationship between the dosage of different drugs. To provide a reference for clinical researchers in the actual use of drugs between the proportion of the relationship.

OBJECTIVE: To mine the core prescription of traditional Chinese medicine for lung cancer medical record. METHODS: Deep FP-Growth algorithm, an improved FP-Growth algorithm, was implemented under the Spark environment and was used to excavate the frequent patterns and association rules of traditional Chinese medicine in the treatment of lung cancer cases. RESULTS: The core prescription of several diseases with higher frequency of occurrence was excavated. CONCLUSION: Deep FP-Growth algorithm based on Spark platform can effectively excavate the core prescription of traditional Chinese medicine in the treatment of lung cancer.

A novel index structure based on the generalized suffix tree (PIGST) is proposed. Combined with post lists, PIGST can answer both structural and content queries. The distinct paths in an XML collection are mapped into strings. The construction algorithm of the PIGST for the path strings is presented based on the modification and improvement of a well-known suffix tree construction algorithm that only requires linear time and space complexity. The query process merely needs m character comparisons for direct containment queries, where m is the length of a query string. An efficient processing method for the indirect containment queries that avoids the inefficient tree traversal operation is also presented. Experiments show that PIGST outperforms earlier approaches.