Drug sensitivity prediction method, electronic equipment and computer readable storage medium

Abstract

The invention discloses a drug sensitivity prediction method, electronic equipment and a computer readable storage medium, and relates to the technical field of drug detection. The method comprises the steps: acquiring gene sequencing data and drug feature data of cancer cell tissue to be trained, pre-processing the gene sequencing data according to the drug feature data to obtain gene sample data, performing verification processing according to the gene sample data and the drug characteristic data to obtain a prediction model and a gene prediction list, and performing drug sensitivity prediction on the cancer cell tissues to be detected through the gene prediction list and the prediction model. Therefore, drug reactivity prediction on clinical patients can be quickly and accurately realized, the prediction cost and the time cost are reduced, the prediction efficiency is improved. and the efficacy prediction efficiency is improved.

Description

technical field [0001] The present application relates to the technical field of drug detection, in particular to a method for predicting drug sensitivity, electronic equipment, and a computer-readable storage medium. Background technique [0002] In the era of precision medicine, the prediction of drug responsiveness of cancer patients based on patients' clinical characteristics and genomics is crucial to assist clinicians in formulating effective and low-toxicity treatment options. Predictive models for drug response are often trained on different datasets. Currently, the most widely used drug prediction models are based on supervised learning techniques, and the supervised learning methods used include regression models and classification models. The former can generate specific drug sensitivity values, such as IC50 (The half maximal inhibitory concentration, half inhibitory concentration), and the latter can generate the level of drug response, such as high-sensitivity ...

AI Technical Summary

Problems solved by technology

However, the current research and solutions are still far from practical application, and cannot be efficiently applied to clinical scenarios

For example, regarding the use of supervised learning to predict drug responsiveness based on the genome or transcriptome, there are certain deficiencies: data analysis is limited to existing databases, and lacks experimental and clinical validation; the method is based on RNA sequencing technology rather than a small gene set , it is impossible to apply rapid gene expression measurement methods, and RNA sequencing takes days to weeks, which is not suitable for the situation of intraoperative or immediate postoperative medication that is often required in clinical practice; drug effect prediction only stops at data analysis, and does not propose Specific and fast application plan, practical application is difficult, costly and time-consuming

Images