Cerebrospinal fluid protein prediction method based on deep neural network

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

Inactive Publication Date: 2020-02-14
JILIN UNIV
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  • Application Information

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However, at present, there is still a gap in the kno

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  • Cerebrospinal fluid protein prediction method based on deep neural network
  • Cerebrospinal fluid protein prediction method based on deep neural network
  • Cerebrospinal fluid protein prediction method based on deep neural network

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Embodiment Construction

[0048] The prediction method of the cerebrospinal fluid protein based on deep neural network, comprises the following steps:

[0049] 1. The establishment of the data set

[0050] (1) Positive sample data set collection

[0051] The protein information in cerebrospinal 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.

[0052] (2) Negative sample data set collection

[0053] 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 10 proteins in the family in the remaining protein family information database, and randomly select 10 protein information from these protein families Negative samples for model training.

[0054] (3) Model training data set segmentation

[0055] The sample data of all positi...

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Abstract

The invention discloses a cerebrospinal fluid protein prediction method based on a deep neural network, which belongs to the technical field of artificial intelligence and big data. The method comprises the following steps of: taking a protein list which is verified by a biological experiment in cerebrospinal fluid of the existing literature and database as a positive sample for model training, deleting protein family information corresponding to the positive sample from a Pfam protein family information database, searching protein families with more than 10 proteins in the families from the remaining protein family information database, and randomly selecting 10 pieces of protein information from the protein families as negative samples for model training. The positive sample data and thenegative sample data are divided into a training set, a verification set and a test set; and feature selection is carried out on protein features, a model is built, the model is trained by using thetraining set, parameter adjustment is carried out on the verification set, and performance evaluation is carried out on the test set. The input is protein characteristics, and the output is a prediction result. The accuracy of cerebrospinal fluid prediction is improved, and finally cerebrospinal 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 cerebrospinal fluid proteins based on a deep neural network. Background technique [0002] Cerebrospinal fluid is a colorless and transparent liquid produced by the choroid plexus in the ventricle, which circulates on the surface of the brain and spinal cord, and is connected with the systemic circulation through the internal cerebral venous system. The main functions are ①protecting the brain and spinal cord from external concussion damage; ②regulating changes in intracranial pressure; ③supplying nutrients to the brain and spinal cord and transporting metabolites; ④regulating the alkali reserves of the nervous system and maintaining normal pH values, etc. [0003] When lesions and traumas occur in brain tissue or spinal cord, cerebrospinal fluid will also undergo various changes. By predicting the proteins in cer...

Claims

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Application Information

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IPC IPC(8): G16B25/10G16B40/00
CPCG16B25/10G16B40/00
Inventor 邵丹王岩黄岚何凯崔薛腾张双全
Owner JILIN UNIV
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