Résumé
With the development of high-throughput sequencing technology, the high-dimensional massive data obtained in omics study puts forward new requirements for statistical analysis. In this case, the traditional theory of single hypothesis testing is no longer applicable, and the issue of multiple hypothesis testing has received increasing attention. This paper introduced three commonly used error measures in multiple testing-family-wise error rate (FWER), false discovery rate (FDR), and positive false discovery rate (pFDR), and the control process in radiobiological omics data analysis, in order to provide a reference for statistical analysis of radiobiological data.
Résumé
In the current clinical medication, there is no method to confirm the sensitivity of patients with different genotypes to corresponding anticancer-drugs and also lack of response to side effects and drug resistance. In recent years, the development of high-throughput technology has made it possible to screen chemotherapeutic drugs on a large scale in cancer cell lines, and generated a large number of omics-data. Currently, based on these data, many predictive models of anticancer-drugs have been established, which will be helpful to predict and optimize the drug targets for cancer patients. This paper reviews the research progress of anticancer-drug prediction models.