Seaweed carbon sequestration protein prediction method and system based on machine learning

A technology of machine learning and prediction methods, applied in the fields of genomics, proteomics, instruments, etc., can solve the problems of human error, time-consuming and other problems, achieve better performance, scientific and reasonable results, and save manpower and material resources.

Active Publication Date: 2021-02-02
SHANDONG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

Based on biological sequences, the structure and function of organisms can be well analyzed and studied. At present, the relevant research in the field of algal carbon-fixing proteins is mostly carried out by traditional biochemical experimental methods. This method takes a long time and requires a lot of manpower and material resources. Time cost, and easy to introduce human error

Method used

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  • Seaweed carbon sequestration protein prediction method and system based on machine learning
  • Seaweed carbon sequestration protein prediction method and system based on machine learning
  • Seaweed carbon sequestration protein prediction method and system based on machine learning

Examples

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Embodiment 1

[0048] In one or more embodiments, a machine learning-based prediction method for seaweed carbon fixation protein is disclosed, referring to figure 1 , including the following steps:

[0049] Step (1): obtaining marine algal protein sequence data, and performing feature extraction on the data;

[0050] Specifically, a single feature extraction strategy can only obtain one-sided information, and different kinds of feature extraction methods can complement each other to obtain valuable information on protein samples.

[0051] In this embodiment, various features extracted from functional groups, Shannon entropy, physical and chemical properties and sequence composition are used to describe protein samples numerically, and all protein sequences are converted into digital feature vectors; the feature extraction strategy includes the following Aspects:

[0052] 1) Functional group. Functional groups determine the chemical properties of organic compounds. The 20 kinds of amino a...

Embodiment 2

[0094] In one or more embodiments, a machine learning-based algal carbon fixation protein prediction system is disclosed, comprising:

[0095] A device for obtaining marine algal protein sequence data and performing feature extraction on the data;

[0096] A device for inputting the extracted features into the trained machine learning classifier after screening;

[0097] A device for outputting prediction results of algal carbon-fixing proteins.

[0098] It should be noted that, the specific implementation process of the above-mentioned devices is implemented by the method disclosed in the first embodiment, and will not be repeated here.

Embodiment 3

[0100] In one or more embodiments, a terminal device is disclosed, including a server, the server includes a memory, a processor, and a computer program stored on the memory and operable on the processor, and the processor executes the The program realizes the method in the first embodiment. For the sake of brevity, details are not repeated here.

[0101] It should be understood that in this embodiment, the processor can be a central processing unit CPU, and the processor can also be other general-purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate array FPGA or other programmable logic devices , discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor, or the processor may be any conventional processor, or the like.

[0102]The memory may include read-only memory and random access memory, and provide instructions and data to ...

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Abstract

The invention discloses a seaweed carbon sequestration protein prediction method and system based on machine learning. The method comprises the steps: acquiring marine algae protein sequence data, andcarrying out the feature extraction of the data; screening the extracted features, and inputting the screened features into a trained machine learning classifier; and outputting a prediction result of the seaweed carbon sequestration protein. According to the method, a machine learning algorithm is adopted to predict whether the protein has the carbon sequestration function or not, compared witha mode of analyzing a large number of biological sequences based on a traditional biochemical experiment, manpower and material resources can be effectively saved, introduction of personal errors to interfere with a result or cause interference is avoided, and the method has higher prediction efficiency and accuracy.

Description

technical field [0001] The invention relates to the technical field of seaweed carbon-fixation protein prediction, in particular to a method and system for predicting seaweed carbon-fixation protein based on machine learning. Background technique [0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art. [0003] Carbon-fixing proteins play a key role in algal carbon fixation. Accurate and rapid identification of algal carbon-fixation proteins is of great significance for the study of algal carbon-fixation mechanisms and the establishment of marine biological carbon pumps. Based on biological sequences, the structure and function of organisms can be well analyzed and studied. At present, the relevant research in the field of algal carbon-fixing proteins is mostly carried out by traditional biochemical experimental methods. This method takes a long time and requires a lot of manp...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16B20/00G16B40/00
CPCG16B20/00G16B40/00
Inventor 高瑞张甘刘治平
Owner SHANDONG UNIV
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