This invention belongs to the interdisciplinary field of
bioinformatics and
traditional Chinese medicine (TCM), and discloses an
algorithm for the improvement and prediction of TCM compound prescriptions, solving the problems of strong subjectivity, long cycle, and low efficiency in traditional TCM compound prescription improvement. The
algorithm first acquires and preprocesses
omics sequencing data from
disease and healthy groups, then establishes TCM-
target gene associations based on the HIT2 and TCMSP databases, and constructs a three-dimensional association matrix of TCM-target genes-5hmC loci for epigenomic data. After
dimensionality reduction and extraction of core principal components using PCA, the sample-TCM association
score is calculated and a matrix is constructed. The p-value is obtained through t-test or Wilcoxon rank-sum test, and converted into DAS values to quantify the regulatory activity of TCM, thus screening TCM for compound prescription improvement. This invention's
algorithm is objective, highly targeted, efficient, and widely applicable, adaptable to various
omics data and
disease scenarios. Its effectiveness has been verified in a
coronary atherosclerosis case, providing precise
technical support for TCM compound prescription screening and optimization, personalized TCM treatment, and the development of innovative TCM drugs.