Research On The Application Of Machine Learning In Drug Delivery And Cell Classification Problem

With the rapid development of genomics,molecular biology has become the mainstream of the development of contemporary life science.This paper will study the delivery system and cell classification of nucleic acid drug therapy at the molecular level.This study is of great significance for the diagnosis and treatment of diseases,and mainly solves the following problems.In the delivery of nucleic acid drugs(mRNA drugs),this paper uses statistical methods to estimate the drug escape that is difficult to observe in the delivery system.Firstly,approximate Bayesian algorithm(ABC),ABC-MCMC algorithm and ABC-SMC algorithm are used to estimate the escape of mRNA drugs respectively.Then the ABC-SMC algorithm is improved from the selection of sampling and tolerance,and an adaptive ABC method is proposed to estimate the drug escape.By comparing with the above algorithms,it is found that the adaptive ABC method not only ensures the accuracy,but also reduces the sampling times and has higher time efficiency.Finally,the new algorithm is applied to the parameter estimation of deterministic biological dynamic system,and the experimental results also prove the universality of the algorithm.In the research of cell classification,this paper uses unsupervised single cell analysis and machine learning methods to realize cell classification.Firstly,unsupervised single cell analysis is used to cluster the high-dimensional gene expression matrix by PCA dimensionality reduction,so as to extract the characteristic genes.Then,K-nearest neighbor and naive Bayesian classification methods are used to analyze the characteristic genes,so as to complete the classification of cells.The experimental data show that the classification success rate of the method used in this paper is more than 93%,which has certain guiding significance for the research of cell type identification.