The invention provides a construction method of a predictive
cell senescence model, a method for predicting the
senescence state of a
tissue sample based on the
senescence model, a method for screening potential therapeutic drugs, equipment, a medium and a program product, and relates to the field of intelligent
medical treatment. The model construction method comprises the following steps: acquiring a
training set sample expression profile
data set; identifying a key senescence
gene set from the
data set by using a
feature selection algorithm; inputting the key senescence
gene set into a
machine learning model to fit a prediction model, and determining an optimal hyper-parameter to obtain a
cell senescence model containing the weight of a
single gene in the key senescence
gene set; the
cell senescence model is a senescence
score obtained by calculating the sum of the product of the expression quantity of a
single gene and the regression coefficient thereof. The cell senescence model, namely PreCSenM, is constructed by integrating a plurality of senescence characteristic
gene sets and a gene
scoring algorithm, the accuracy in CS evaluation is superior to that of 10 existing methods, and the application of CS from biological research to clinical scenes is also realized.