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Cancer driver gene prediction method

A technology for driving genes and cancer, applied in the fields of genomics, machine learning, instruments, etc., can solve the problems such as the accuracy rate cannot meet the clinical needs, lack of actual verification, and no further refinement of the prediction of driver gene functions, etc., to improve the prediction accuracy rate. Effect

Active Publication Date: 2021-10-19
海南精准医疗科技有限公司
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Problems solved by technology

This gene classification algorithm is relatively simple and lacks practical verification, and the function of the predicted driver genes has not been further refined, so the correct rate of prediction cannot meet the clinical needs

Method used

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  • Cancer driver gene prediction method
  • Cancer driver gene prediction method

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Embodiment Construction

[0020] Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that the relative arrangements of components and steps, numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise.

[0021] The following description of at least one exemplary embodiment is merely illustrative in nature and in no way taken as limiting the invention, its application or uses.

[0022] Techniques, methods and devices known to those of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, such techniques, methods and devices should be considered part of the description.

[0023] In all examples shown and discussed herein, any specific values ​​should be construed as exemplary only, and not as limitations. Therefore, other instances of the exemplary embodiment may have dif...

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Abstract

The invention discloses a cancer driver gene prediction method. The method comprises the steps: constructing a first data set and a second data set, wherein the first data set represents the incidence relation between gene features and driving gene mutation types, and the second data set represents the incidence relation between the gene features and driving function types; training a first machine learning classification model by using the first data set, and predicting a new driver gene; confirming data corresponding to the new driver gene predicted by the first machine learning classification model as a second prediction data set; training a second machine learning classification model by using the second data set, and predicting the second prediction data set by using the trained second machine learning classification model to predict the driving function of the new driver gene. According to the invention, the prediction accuracy and the generalization ability of model application can be effectively improved.

Description

technical field [0001] The present invention relates to the technical field of machine learning, and more specifically, to a method for predicting cancer driver genes. Background technique [0002] Driver genes are important genes related to the occurrence and development of cancer, and precision medicine based on driver genes is an important direction for the treatment of cancer. By analyzing the changes in gene expression levels in cells during cancer formation, it can be found that some genes can control tumors. If these gene expressions or gene pathways are inhibited, the events related to tumor development can be terminated. These genes are called cancer drivers. Gene. Driver genes are the most important internal cause of cancer. Targeted therapy of driver genes can achieve twice the result with half the effort. In the era of precision medicine, identifying driver mutations in a patient's tumor cells is a central task. [0003] In the prior art, based on the gene mut...

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Application Information

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IPC IPC(8): G16B20/20G06K9/62G06N20/00
CPCG16B20/20G06N20/00G06F18/24155
Inventor 代小勇苏明
Owner 海南精准医疗科技有限公司
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