The invention discloses a
data processing method and
system for tumor
cell detection, and relates to the technical field of
data processing.The method comprises the steps that cells are sequenced, and
cell metabolism is monitored; performing time
delay reconstruction according to the embedding dimension and the
delay time, mapping
gene expression data to a reconstruction
phase space, and calculating a chaos degree of
gene expression; establishing a biomarker white
list, inputting the chaos degree into an adaptive weight
distributor, setting feature retention weights for markers in the white
list, constructing a protective auto-
encoder, dynamically adjusting the dimension of a
bottleneck layer, and performing forced constraint when the markers in the white
list are detected; the method comprises the following steps: extracting
coupling features through metabolic oscillation data and physical microenvironment data, calculating gradient sensitivity of
shear stress to metabolic oscillation, carrying out
feature fusion on the
coupling features and
gene expression features, and constructing a
subtype classification boundary for the fused features through
feature vector selection in combination with the gradient sensitivity. Accurate dimension reduction of data and deep analysis of biological characteristics are realized.