Prognosis prediction method and system based on gene big data

A technology of genetic data and prediction methods, applied in the field of artificial intelligence, can solve the problem of undiscoverable genes affecting prognosis

Pending Publication Date: 2020-03-31
SHANDONG UNIV
View PDF9 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Although some studies have shifted the research focus to the level of gene characteristics, traditional statistical methods are used to select gene charac

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Prognosis prediction method and system based on gene big data
  • Prognosis prediction method and system based on gene big data

Examples

Experimental program
Comparison scheme
Effect test

Example Embodiment

[0025] Example 1:

[0026] A prognostic prediction method based on gene big data, including the following steps:

[0027] (1) Data collection and fusion;

[0028] Collect fresh or frozen cancer tissue samples from patients and perform sequencing to obtain genetic data, and obtain clinical data on survival time and survival status of patients according to follow-up investigations; this example uses public data sets: taking lung adenocarcinoma as an example, from public databases TCGA downloads lung adenocarcinoma LUAD related data https: / / portal.gdc.cancer.gov / , including genetic data and clinical data;

[0029] The genetic data is fused with clinical data, and the clinical data that is the survival time data is matched according to the sample name. The samples with missing survival time are deleted. After the raw counts are obtained from the sequencing, the genetic data is standardized to FPKM ( Fragments Per Kilobase Million) format data for subsequent processing.

[0030] (2) Screen...

Example Embodiment

[0040] Example 2:

[0041] A prognosis prediction system based on genetic big data, including a data preprocessing module, a screening module, and a training verification module. The data preprocessing module is used to download and standardize data from a public database TCGA into FPKM format data, the data including genetic data And clinical data; the screening module is used to screen the data according to two types of conditions, the two types of conditions are the prescribed conditions of clinical data and the prescribed conditions of genetic data; the training verification module includes at least two algorithm models, and the training verification module is used for Reclassify the samples filtered by the screening module, use the relief algorithm to rank the genes, and train the input data of different algorithm models, compare the results of different algorithm models through the training verification module, and select the algorithm with the highest accuracy The model an...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention relates to a prognosis prediction method and system based on gene big data, and belongs to the technical field of artificial intelligence. The method mainly comprises the following steps: extracting gene information in a tissue sample to form a training set, sorting gene importance by using a reelief algorithm, carrying out fitting classification on prognosis time by using a machinelearning algorithm model, and selecting an algorithm model with the highest accuracy and a gene characteristic number as gene characteristic numbers and prediction methods of a specific disease. According to the method, new gene data can be rapidly tested after model training is completed, and prognosis evaluation can be facilitated.

Description

technical field [0001] The invention relates to a cancer prognosis prediction method and a prediction system based on gene big data, belonging to the technical field of artificial intelligence. Background technique [0002] Lung cancer accounts for 1 in 4 cancer deaths, according to annual statistics reported by the American Cancer Society. Although previous scholars have obtained a large amount of data from microarray technology and next-generation sequencing (NGS), the information in these data may not be fully explored. Traditional survival predictions depend on the patient's clinicopathological features and are sometimes imprecise. [0003] In recent years, with the development of next-generation sequencing technology, we have been able to obtain large-scale gene sequencing data of cancer samples, and the development of big data and artificial intelligence has made it possible for us to mine valuable potential information from this massive data. At present, for the pre...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G16B30/00G16B40/00G06N20/00
CPCG16B30/00G16B40/00G06N20/00
Inventor 张海霞刘艺迪袁东风
Owner SHANDONG UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap