Classification method of differential proteomics

Abstract

The invention belongs to the field of proteomics classification, relating to a classification method of differential proteomics. The method comprises: selecting characteristics by univariate statistics analysis, sequential feature selection and a genetic algorithm; extracting characteristics by main ingredient analysis and a partial least squares method; connecting and integrating with linear discriminant analysis, a k-nearest neighbor classifier, a support vector machine, a decision tree, a naive Bayes classifier and an artificial neural network in series to obtain serial integrated classifiers which are connected in parallel and combined; endowing each base classifier with a weighting coefficient according to classification accuracy rate; and taking a fuzzy attribute value as a classification result output mode to obtain the classification judgment result of a target sample. The method has higher classification accuracy rate and robustness on classifying and judging abnormal and normal samples in the differential proteomics, and is suitable for classifying and analyzing the data of differential proteomics.

Description

Technical field: [0001] The invention belongs to the field of biotechnology and proteomics classification, and relates to a differential proteomics classification method. Background technique: [0002] An important direction in differential proteome research is to mine disease proteome data for bioinformatics and establish classification models to classify, judge and predict clinical samples. With the development of high-throughput research technologies, the scale of omics data output accumulation is growing rapidly. Therefore, it has become a general trend to use computer methods to automatically classify and judge clinical samples. The existing technology mainly includes the following steps in the computer classification and judgment of differential proteomes: after obtaining the original data, first perform data preprocessing, then perform feature space optimization on the training data set and select or extract feature variables, and then obtain the feature The data is...

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