Stem cell activity detection system based on svm algorithm

Abstract

The invention provides a stem cell activity detection system based on a svm algorithm. The system comprises a subset generation unit, a subset evaluation unit, a stop condition unit and a result verification unit. A support vector machine is widely applied to each filed of machine learning. Correlation problems of classification and mode identification can be solved, and problems of regression, fitting, density estimation and the like can be effectively processed. Compared to other existing machine learning algorithms, the SVM has a solid theory foundation and research and application are wide. The SVM is a small sample learning algorithm based on a statistics learning theory, structure risk minimization is taken as a target and a generalization capability is increased, and under limited sample information, an optimal solution of the problem can be acquired.

Description

technical field [0001] The invention relates to the field of stem cells, in particular to a stem cell activity detection system based on an svm algorithm. Background technique [0002] Apoptosis is the opposite of cell proliferation, and it discusses the way and mechanism of cell death. The word apoptosis comes from Greek. Originally refers to the shedding of petals and leaves. Since Kerr first proposed the concept of apoptosis in 1972, with the development of cell biology, immunology and oncology research, people have a deeper understanding of the important theoretical and practical significance of apoptosis in recent years. Apoptosis refers to the process of automatic death of cells through gene regulation in order to maintain the stability of the internal environment under certain physiological and pathological conditions. Different types of cells have different kinetic processes when apoptosis occurs, and there are certain morphological and biochemical differences, bu...

AI Technical Summary

Problems solved by technology

As a common way of dimensionality reduction, selection technology has been deeply studied and widely used, but there is no fixed mode and unified mathematical definition for feature selection. Due to different research objects and task requirements, feature selection algorithms focus on Even for a relatively clear problem, the meaning or purpose of feature selection may be different. Many researchers start from different angles of the problem.

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