The invention relates to the field of noninvasive
prenatal diagnosis, and particularly discloses a
preeclampsia noninvasive
screening method based on
deep sequencing 8bp
oligonucleotide double-fragment characteristics, which comprises the following steps: collecting
preeclampsia and healthy pregnant woman
peripheral blood samples, and extracting
free DNA for high-
throughput sequencing; the method comprises the following steps: extracting core 8-mer sequences' GTGCGCCC 'and' GATGGGGT 'in a long fragment of 150-200bp through
bioinformatics analysis; an integrated
support vector machine, K-nearest neighbor, extreme gradient lifting, a
random forest and a multi-layer
perceptron are combined with a
logistic regression element classifier to construct a stacking model, the frequency of a core sequence is normalized,
machine learning analysis is carried out, and the
preeclampsia risk is predicted. According to the invention, two 8bp
oligonucleotide characteristic fragments are specifically screened, and a
deep learning architecture of multi-model fusion is combined, so that the limitations of low specificity and invasive detection of a traditional
screening method are effectively broken through.