Amniotic fluid protein prediction method based on recurrent neural network

A technology of cyclic neural network and prediction method, which is applied in the field of big data and artificial intelligence to achieve the effect of improving accuracy

Inactive Publication Date: 2020-02-21
JILIN UNIV
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  • Claims
  • Application Information

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But at present, there is still a gap in the known comp

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  • Amniotic fluid protein prediction method based on recurrent neural network
  • Amniotic fluid protein prediction method based on recurrent neural network
  • Amniotic fluid protein prediction method based on recurrent neural network

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Embodiment Construction

[0050] The prediction method of the amniotic fluid protein based on the recurrent neural network comprises the following steps:

[0051] 1. The establishment of the data set

[0052] (1) Positive sample data set collection

[0053] The protein information in amniotic fluid that has been verified by biological experiments is obtained by searching biologically relevant literature and existing databases as positive samples for model training and entered into the computer.

[0054] (2) Negative sample data set collection

[0055] Delete the protein family information corresponding to the positive sample in step 1 in the Pfam protein family information database, search for protein families with more than 5 proteins in the family in the remaining protein family information database, and randomly select 5 protein information from these protein families Enter the computer as negative samples for model training.

[0056] (3) Model training data set segmentation

[0057] The sample da...

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Abstract

The invention discloses an amniotic fluid protein prediction method based on a recurrent neural network, and belongs to the technical field of big data and artificial intelligence. According to the method, a protein list verified by biological experiments in amniotic fluid of existing literatures and databases is used as a positive sample for model training; protein family information corresponding to the positive sample is deleted from a Pfam protein family information database, protein families with the number of proteins exceeding 5 in the families are searched for in the remaining proteinfamily information database, and five pieces of protein information are randomly selected from the protein families to serve as negative samples for model training. The method further comprises the steps of: dividing the positive sample data and the negative sample data into a training set, a verification set and a test set; and carrying out feature selection on protein features, building a model,training the model by using the training set, carrying out parameter adjustment on the verification set, and carrying out performance evaluation on the test set. The input is a protein feature and the output is a prediction result. The accuracy of amniotic fluid prediction is improved, and finally amniotic fluid protein prediction is achieved.

Description

technical field [0001] The invention belongs to the technical fields of big data and artificial intelligence, and in particular relates to a method for predicting amniotic fluid protein based on a recurrent neural network. Background technique [0002] Amniotic fluid is a colorless and transparent alkaline liquid, more than 90% of which is water, and it also contains minerals, urea, uric acid, creatinine, vernix, and fetal epithelial cells. The amount of AFP in amniotic fluid can be used as an index to monitor whether the fetus has abnormalities. Through the detection of fetal cell chromosomes in amniotic fluid, the fetus can be screened for genetic diseases. [0003] Some specifically expressed protein markers were found in amniotic fluid, so that early diagnosis of pregnancy-related diseases such as amniotic fluid embolism can be performed. It can be said that the expression of certain proteins in amniotic fluid is very meaningful, and they reflect the physiological and p...

Claims

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Application Information

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IPC IPC(8): G16B40/00G16B50/00G06N3/04G06K9/62
CPCG16B40/00G16B50/00G06N3/045G06N3/044G06F18/241
Inventor 王岩何凯邵丹黄岚王尧张睿
Owner JILIN UNIV
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