Biological sample intelligent identification method based on molecular map

A biological sample and intelligent identification technology, applied in the field of intelligent identification of biological samples based on molecular maps, can solve the problems of lack, difficulty in meeting the classification and identification requirements of large biological samples, and excess features, so as to achieve fast analysis speed, improve feasibility and Efficiency, increased accuracy and speed effects

Active Publication Date: 2019-05-21
DALIAN INST OF CHEM PHYSICS CHINESE ACAD OF SCI
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  • Application Information

AI Technical Summary

Problems solved by technology

However, current classification methods, adapted to single molecules (features), are highly intensity-centric and often require labor-intensive structure identification 3,7 , and suffers from the excess of features 8
Therefore, their use is generally limited to limited sample sets of species of interest, and almost to date, there is still a lack of general techniques and means for high-throughput and high-accuracy analysis of biological samples of different types and sources, and it is difficult to meet the requirements of large biological samples. classification and identification requirements

Method used

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  • Biological sample intelligent identification method based on molecular map
  • Biological sample intelligent identification method based on molecular map
  • Biological sample intelligent identification method based on molecular map

Examples

Experimental program
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Effect test

Embodiment 1

[0059] The following example demonstrates how to use molecular map-based point cluster matching technology to identify and classify ginseng, American ginseng, red ginseng, panax notoginseng, achyranthes bidentata, rehmannia glutinosa, sophora flavescens, and astragalus with high precision.

[0060] The purpose of this example is to demonstrate how to use molecular map generation technology, point density-based clustering technology, image segmentation technology, cluster matching technology, machine classification technology, etc. to achieve efficient and accurate identification of samples.

[0061] Materials and Methods:

[0062] Chinese medicine samples from the market: ginseng (1; number of samples n=170), American ginseng (2; number of samples n=100), red ginseng (3; number of samples n=100), Panax notoginseng (4; number of samples n=100 ), Achyranthes bidentata (5; sample number n=100), Rehmannia glutinosa (6; sample number n=100), Sophora flavescens (9; sample number n=1...

Embodiment 2

[0091] The following example demonstrates how to use molecular map-based point cluster matching technology to identify and classify Albizia juliensis and Albizia juliensis with high precision.

[0092] The purpose of this example is to demonstrate how to use molecular map generation technology, point density-based clustering technology, image segmentation technology, cluster matching technology, machine classification technology, etc. to achieve efficient and accurate identification of samples.

[0093] Materials and Methods:

[0094] U, Chinese medicine samples from the market Albizia Julibrissin (n=100), Albizia Julibrissin (n=100) as unknown samples (sample to be tested); =52), Sophora flavescens (9; n=192), Astragalus membranaceus (10; n=212) as training samples, and another 10 kinds of medicinal materials (ginseng, American ginseng, red ginseng, Panax notoginseng, Achyranthes bidentata, Rehmannia glutinosa, Acacia bark, Albizia julibrissin, Sophora flavescens, Astragalus...

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Abstract

The invention relates to a biological sample intelligent identification method based on a molecular map. An existing classification method is limited to a limited biological sample set, a universal technology and means capable of analyzing biological samples of different types and different sources in a high-throughput and high-accuracy mode are lacked, and the classification and recognition requirements of biological large samples are difficult to meet. The invention provides an intelligent identification method based on a molecular map. According to the invention, spatial characteristics such as point clusters and shapes hidden in a biological sample molecular map are utilized to carry out high-efficiency identification on the biological sample. Points with high density are clustered into point clusters through clustering, high-precision scanning and matching between unknown samples and standard samples are carried out through fine space information of the point clusters, and recognition is carried out by comparing the matching degrees; the result shows that the classification effect is obviously superior to that of the reported method based on the spatial characteristics contained in the molecular map.

Description

technical field [0001] This field relates to the intelligent identification of biological samples, in particular to a method for intelligent identification of biological samples based on molecular maps. A powerful tool for samples; the invention makes full use of technologies such as machine learning and image recognition to improve the accuracy and speed of identification and classification, and realize reliable identification and classification of large batches of biological samples. Background technique [0002] There is a growing need to classify unknown biological samples in many application areas. Due to the explosive growth of sample size, developing fast, accurate and complex classification techniques has become a very challenging subject 1-3 . Because of the extremely complex composition of biological samples, many different methods have been developed for this purpose. To sum up, there are mainly four methods: genetic method, chromatography, spectrometry and omi...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01N30/88G01N27/62G01N33/483
Inventor 张晓哲赵楠
Owner DALIAN INST OF CHEM PHYSICS CHINESE ACAD OF SCI
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