Organ chip database vectorization scheme for artificial intelligence algorithm

A technology of organ chip and artificial intelligence, applied in the field of organ chip database vectorization, can solve the problems of not carefully analyzing data association, loss, lack of time and energy, etc.

Pending Publication Date: 2022-05-24
TIANJIN POLYTECHNIC UNIV
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AI Technical Summary

Problems solved by technology

[0003] During the process of organ chip culture and experimentation, a large amount of experimental data will be generated, but in previous studies, researchers did not carefully analyze the relationship between the data, especially the relationship between different organ chip experiments. , the data is not shared, so they can’t pay attention to the data correlation characteristics between different experiments. They just

Method used

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Embodiment Construction

[0019]For the organ-on-a-chip database, it should contain data tables related to drug information (storing drug names, molecular formulas, 2D and 3D structural formulas, target proteins, SMILE format expressions, MOL2VEC codes, etc. The target protein information needs to establish a protein data table) and target data table to express Drug Target Interaction, referred to as DTI related information), data table related to cell information (storing cell line name, source, gene sequence, GENE2VEC code, etc.), data table related to bioscaffold material information (stored Molecular formula, structural formula, coding representation, etc.), data tables related to biological reagent information (storing ingredients, ratios, concentrations, chemical formulas, structural formulas, codes, etc.), data tables related to organ chip models (storing chip model ID, enumeration, etc.) Organ chip type, developer, institution, article name and link, official website introduction link, chip stru...

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Abstract

The patent mainly describes a method suitable for organ chip database vectorization. The organ chip database comprises biological scaffold materials, reagents, cell lines, drugs, organ chip models and organ chip configuration parameters (concentrations of the reagents and the drugs, cell types and the like); a deep learning model weight matrix can be obtained through data training, and time information and experimental results (cell metabolite concentration, cell number and survival rate, PH, temperature, oxygen concentration, carbon dioxide concentration, TEER and air pressure of a microenvironment in a chip, whether drugs are added or not, and drug release rate and degradation rate) as label data; after the information is input, the model can automatically predict label data. The data in the database needs to be input into the model, data format conversion is firstly carried out, data types and formats stored in the organ chip database are not uniform, text information, digital information and even image information exist, and the information needs to be converted into vector information which can be recognized by a machine learning algorithm. In order to solve the problem, how to vectorize the data of the organ chip is designed.

Description

technical field [0001] The invention belongs to the field of biomedical engineering and computer science medical-engineering integration, and designs a method suitable for vectorization of an organ chip database. The organ-on-chip database contains bioscaffold materials, reagents, cell lines, drugs, organ-on-chip models, organ-on-chip configuration parameters (reagent and drug concentrations, cell types, etc.), as well as time information and experimental results (cellular metabolites) as label data. Concentration, number and survival rate of cells, PH, temperature, oxygen concentration, carbon dioxide concentration, TEER, air pressure, drug addition, drug release rate, degradation rate) of the microenvironment in the chip, deep learning can be obtained after training with these data Model weight matrix, when the above information is input, the model will automatically predict the label data. For the data in the database to be input into the model, the first thing to do is to...

Claims

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Application Information

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IPC IPC(8): G16B50/30G16B40/00G16H10/40G06F17/16
CPCG16B50/30G16B40/00G16H10/40G06F17/16
Inventor 马欣林文斌
Owner TIANJIN POLYTECHNIC UNIV
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