Clinical omics data processing method and device based on machine learning

Abstract

The invention relates to a clinical omics data processing method based on machine learning. The clinical omics data processing method comprises the following steps: acquiring to-be-processed omics data; extracting a combination of omics features from the to-be-processed omics data as a feature subset; performing omics feature analysis according to the feature subset to obtain a model verificationresult; determining the importance degree value of the omics feature according to the influence amplitude of the omics feature on the model verification result value; screening out a target feature subset according to the importance degree value of the omics features; and according to the target feature subset, carrying out omics feature analysis on the omics data to obtain a predicted value usedfor representing a classification or a physiological index corresponding to the omics data. According to the method, the omics data processing efficiency can be improved. The embodiment of the invention further provides clinical omics data processing, a server and a storage medium.

Description

technical field [0001] The present invention relates to the field of computer technology, in particular to a machine learning-based clinical omics data processing method, device, server and storage medium. Background technique [0002] Machine learning (Machine Learning, ML) is a multi-field interdisciplinary subject, involving probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory and other disciplines. Specializes in the study of how computers simulate or implement human learning behaviors to acquire new knowledge or skills, and reorganize existing knowledge structures to continuously improve their performance. Machine learning is the core of artificial intelligence and the fundamental way to make computers intelligent, and its application pervades all fields of artificial intelligence. Machine learning and deep learning usually include techniques such as artificial neural network, belief network, reinforcement learning, transfe...

AI Technical Summary

Problems solved by technology

[0005] In view of this, it is necessary to provide a clinical omics data processing method, device, server and storage medium based on machine learning, which can solve the problem of low processing efficiency of omics data processing and application in the prior art

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