Marker for predicting hereditary angioedema attack and application thereof
An angioedema and hereditary technology, applied in the field of microbiology and bioinformatics, achieves the effects of short measurement cycle, high sensitivity and reduced incidence
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Embodiment 1
[0055] The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention. Unless otherwise specified, the examples are all in accordance with conventional experimental conditions, such as Sambrook et al. Molecular Cloning Experiment Manual (Sambrook J & Russell DW, Molecular Cloning: a Laboratory Manual, 2001), or in accordance with the conditions suggested by the manufacturer's instructions. The establishment and screening of embodiment 1 model algorithm
[0056] The present invention finds the most suitable model and specific species of bacteria as microbial markers through preliminary screening operations, optimizes the parameters of the model according to the data with class labels, improves the accuracy and sensitivity of the model, and predicts through the output risk value, and can Indicate the balance of intestinal microorganisms, guide the adjustment of individualized intestinal flora, and reduce the ...
Embodiment 2
[0070] The bacterium of embodiment 2 specific species is selected
[0071] 1. The XGBoost model obtains the Feature-importance score of the variable feature ( Figure 5 ), according to the high and low ranking of the score, gradually increase the number of bacterial variables to obtain the variables required for the optimal ROC-AUC. The results show that the ROC-AUC value is the largest when inputting the bacterial abundance of 15 specific species of characteristic variables.
[0072] 2. To test the model, split the data into a training set and a test set, input the bacterial abundance of 15 specific species in the sample, and input it into the Xgboost model. The parameters of the model are optimized according to GridsearchCV, trained with the training set, and tested with the test set.
[0073] 3. The storage model is used for the prediction of disease risk of subsequent measurement data.
[0074] The number and combination of input variables will produce different ROC-AUC. ...
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Abstract
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