Breast cancer lymph node metastasis prediction method and prediction system based on gene spectrum
A technology of lymph node metastasis and prediction method, which is applied in the field of breast cancer lymph node metastasis prediction method and prediction system, which can solve problems such as overtreatment, achieve the effect of improving accuracy and avoiding overtreatment
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Embodiment 1
[0044] A method for predicting lymph node metastasis of breast cancer based on gene profile. According to an embodiment of the present invention, a method for predicting lymph node metastasis of breast cancer based on the combination of gene expression profile and machine learning method, using gene expression profile and clinical data, finally determines and predicts breast cancer A model of lymph node metastasis.
[0045] figure 1 It is a flowchart of a method for predicting breast cancer lymph node metastasis based on gene expression profile according to an embodiment of the present invention.
[0046] like figure 1 As shown, (1) Enter the GEO platform to obtain RNA data and clinical data, enter the GEO (GeneExpression Omnibus) platform, select the GSE17705 data set, and download the file with the suffix .txt.gz, which contains the RNA data of 298 breast cancer samples And clinical data, from which four types of available information are extracted: gene name, normalized_r...
Embodiment 2
[0067] A prediction system for lymph node metastasis of breast cancer based on gene spectrum, including a data preprocessing module, a feature processing module, and a training verification module, the data preprocessing module is used to obtain a sample data set from the GEO platform, and pre-process the sample data Processing, the sample data set includes RNA data and clinical data, the preprocessing includes sample classification, data conversion, data standardization; the feature processing module is used to select differential genes in the data processed by the data preprocessing module, and A machine learning method is used to select gene features; the training and verification module includes at least two prediction models, and the training and verification module is used to input the difference gene as a feature into the prediction model with the highest prediction accuracy obtained through training.
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