Information recognition of pathogenic modules in gene statistics of big data. Issue 1 (22nd March 2021)

• Record Type: Journal Article
• Authors: Li, Xiaoxia; Chang, Minhui; Wang, Lianhua
• Is Part Of: Nanomaterials and energy. Volume 10:Issue 1(2021)
• Page Start: 35
• Page End: 42
• Publication Date: 2021-03-22
• Subjects: biochip -- environmental impact -- gene; Nanostructured materials -- Periodicals; Nanostructures -- Periodicals; 620.115
• DOI: 10.1680/jnaen.20.00025
• Languages: English
• ISSNs: 2045-9831

Abstract

Abstract : Aiming at the problem that the selection of the F value is too small when the negative outliers are removed by traditional recognition methods, a recognition method of pathogenicity module information in gene statistics of high-dimensional big data is proposed. This method involves using gene chips to obtain gene expression data, constructing a dynamic network to screen pathogenic module genes, preprocessing gene expression data, calculating the maximum information coefficient characteristics of pathogenic module information by using a feature matrix, standardizing the processing of pathogenic module information data, establishing pathogenic module information recognition rules and completing pathogenic module information in gene statistical interest recognition of high-dimensional big data interest. The experimental results show that compared with the traditional recognition methods, the disease module information recognition method in high-dimensional big data gene statistics is less affected by the K value and the actual recognition accuracy is up to 98%.