Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

An intestinal microecology differential bacteria classification processing method and an intestinal health assessment method

A technology of intestinal microecology and processing method, which is applied in the field of intestinal microbial data analysis and processing, can solve the problems of wide distribution and strong interference of intestinal flora data, and achieve the effect of meeting application requirements and strong anti-interference performance

Active Publication Date: 2021-10-08
广西爱生生命科技有限公司
View PDF14 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0012] The technical problem to be solved by the present invention is to provide a method that can solve the problems of wide distribution and strong interference of intestinal flora data, and at the same time obtain key features based on aggregation and classification algorithms, and analyze intermediate Intestinal microecological differential bacteria classification processing method and intestinal health assessment method for further improvement

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
  • An intestinal microecology differential bacteria classification processing method and an intestinal health assessment method
  • An intestinal microecology differential bacteria classification processing method and an intestinal health assessment method
  • An intestinal microecology differential bacteria classification processing method and an intestinal health assessment method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0034] In this embodiment, the step S10 mainly realizes the digital processing process of the sample features.

[0035] For the step S10, it is necessary to collect A data first, including n samples in total, including the non-healthy sample DG marked by industry experts and the control group sample CG, wherein the number of samples of the non-healthy sample DG is n1, and the sample number of the control group is n1. The number of samples of CG is n2, note that the values ​​of n1 and n2 are close, and there is n=n1+n2. Sample characteristics are composed of the basic information of the sample and the absolute abundance of intestinal flora (OTU characteristics) obtained after 16S sequencing, and each sample includes at most m characteristics.

[0036] In the step S10, the characteristics of the n samples are first arranged into a matrix, each row of the matrix is ​​composed of a sample characteristic, and the sample characteristics are expressed as: number+age+sampling address+...

Embodiment 2

[0039] In this embodiment, the step S11 mainly realizes the format conversion and filtering functions of the digitized features.

[0040] For the step S11, a format conversion step and a step of screening out low-abundance features are further included:

[0041] For the format conversion step, the relative abundance of the flora is the ratio of the abundance of a certain flora in the specified sample to the sum of the abundances of all the flora in the sample. Therefore, in the step S11, the absolute Abundance is converted to relative abundance:

[0042] Set the abundance of a certain bacterial group in the sample as R ij , the non-healthy group DG or the control group CG have n1 or n2 samples respectively, and each sample has m features, then the relative abundance of the jth feature in the ith sample is:

[0043]

[0044] Based on the above calculation method, the digital characteristics of each sample were converted from absolute abundance to relative abundance.

[00...

Embodiment 3

[0049] In this embodiment, the step S12 is a sample similarity clustering process.

[0050] For the step S12, it involves the sample similarity clustering process: first, all samples in the sample A set are divided into multiple categories according to the similarity of OTU features, and samples with high similarity are classified into the same category, and are divided into k categories in total. types, and each intestinal type contains multiple samples.

[0051] Further, in the step S12, samples x:[x 1 ,x 2 ,...,x i ,...,x m ] and sample y:[y 1 ,y 2 ,...,y i ,...y m ], use the following formula to calculate the similarity sim of sample x and sample y:

[0052]

[0053] Among them, it is known that the sample set A contains a large number of samples with the same structure as x or y, and the method of data clustering can be used, that is, the samples are divided into multiple groups by calculating the similarity between two samples, and the samples with high simila...

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 discloses an intestinal microecology differential bacteria classification processing method and an intestinal health assessment method, and the method comprises the following steps: S10, collecting a sample set A which at least comprises n samples composed of an unhealthy group sample DG and a control group sample CG; step S11, converting the digital feature of each sample from absolute abundance to relative abundance; step S12, dividing all samples in the sample set A into k groups of intestinal types according to the similarity of absolute abundance, marking an intestinal type set obtained after classification as AB, then separating an unhealthy group sample DG and a control group sample CG in the intestinal type set AB, marking the unhealthy group sample set as DG *, and marking the control group sample set as CG *; and S13, calculating the relative abundance difference between the unhealthy group sample set DG * and the control group sample set CG *, and sorting the relative abundance difference to obtain difference characteristics DF corresponding to the k groups of intestinal types. The invention not only can effectively extract high-dimensional sample features with a large abundance change range, but also is higher in anti-interference performance.

Description

technical field [0001] The invention relates to a method for analyzing and processing intestinal microbiological data, in particular to a method for classifying and processing intestinal microecological differences and a method for evaluating intestinal health. Background technique [0002] In recent years, with the discovery that the gut microbiota not only plays a role in cardiovascular ill-health and metabolic-related ill-health, but also becomes an important environmental factor for some tumors, including colorectal, liver tumors, and breast cancer. With the development of genomics technologies such as next-generation gene sequencing and metabolomics, the role of gut microbiota in tumorigenesis and development has attracted much attention. Health, liver cancer and other non-health screening, prognostic markers and therapeutic targets. [0003] On the other hand, the structure of gut microbiota will show different changes with age, especially age-related changes in diet ...

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
Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G16H50/30G06N20/00
CPCG16H50/30G06N20/00G06F18/23G06F18/22G06F18/24Y02A90/10
Inventor 陈晓春王小军覃涛
Owner 广西爱生生命科技有限公司
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com