Gene Selection Using Neighborhood Rough Set from Gene Expression Profiles

2007 ◽  
Author(s):  
Shulin Wang ◽  
Huowang Chen ◽  
Shutao Li
Keyword(s):  
Gene Expression ◽  
Rough Set ◽  
Gene Selection ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Neighborhood Rough Set

Classification Algorithm on Gene Expression Profiles of Tumor Using Neighborhood Rough Set and Support Vector Machine

2013 ◽  
Vol 850-851 ◽  
pp. 1238-1242
Author(s):  
Tao Chen
Keyword(s):  
Gene Expression ◽  
Rough Set ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Classification Algorithm ◽  
Support Vector ◽  
Data Set ◽  
Filtering Method ◽  
Neighborhood Rough Set ◽  
Feature Filtering

Gene expression profiles of tumor have the limited amount of samples in comparison to the high dimensionality of the samples;this paper proposed a classification algorithm based on neighborhood rough set to improve classification accuracy.This paper first applied feature filtering method of kruskal-wallis rank sum test to select a set of top-ranked related genes, and then applied neighborhood rough set on these genes to generate a informative genes subset. Finally, SVM was used to classify the GEP data set. The result of the experiment indicates that this method can effectively improve classification accuracy, and it has higher generalization.

scholarly journals A Robust Gene selection Method for Microarray-based Cancer Classification

2010 ◽  
Vol 9 ◽  
pp. CIN.S3794 ◽  
Author(s):  
Xiaosheng Wang ◽  
Osamu Gotoh
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Gene Selection ◽  
Information Gain ◽  
Expression Profiles ◽  
Feature Selection Method ◽  
Gene Expression Profiles ◽  
Molecular Classification ◽  
Selection Method ◽  
Chi Square

Gene selection is of vital importance in molecular classification of cancer using high-dimensional gene expression data. Because of the distinct characteristics inherent to specific cancerous gene expression profiles, developing flexible and robust feature selection methods is extremely crucial. We investigated the properties of one feature selection approach proposed in our previous work, which was the generalization of the feature selection method based on the depended degree of attribute in rough sets. We compared the feature selection method with the established methods: the depended degree, chi-square, information gain, Relief-F and symmetric uncertainty, and analyzed its properties through a series of classification experiments. The results revealed that our method was superior to the canonical depended degree of attribute based method in robustness and applicability. Moreover, the method was comparable to the other four commonly used methods. More importantly, the method can exhibit the inherent classification difficulty with respect to different gene expression datasets, indicating the inherent biology of specific cancers.

A ROUGH SET THEORY APPROACH TO THE ANALYSIS OF GENE EXPRESSION PROFILES

2013 ◽  
pp. 51-83
Author(s):  
Joachim Petit ◽  
Nathalie Meurice ◽  
José Luis Medina-Franco ◽  
Gerald M. Maggiora
Keyword(s):  
Gene Expression ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Theory Approach

scholarly journals PCA-based unsupervised feature extraction for gene expression analysis of COVID-19 patients

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kota Fujisawa ◽  
Mamoru Shimo ◽  
Y.-H. Taguchi ◽  
Shinya Ikematsu ◽  
Ryota Miyata
Keyword(s):  
Gene Expression ◽  
Feature Extraction ◽  
Target Genes ◽  
Gene Selection ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Principal Component ◽  
Data Set ◽  
Immune Related Genes ◽  
Unsupervised Feature Extraction

AbstractCoronavirus disease 2019 (COVID-19) is raging worldwide. This potentially fatal infectious disease is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, the complete mechanism of COVID-19 is not well understood. Therefore, we analyzed gene expression profiles of COVID-19 patients to identify disease-related genes through an innovative machine learning method that enables a data-driven strategy for gene selection from a data set with a small number of samples and many candidates. Principal-component-analysis-based unsupervised feature extraction (PCAUFE) was applied to the RNA expression profiles of 16 COVID-19 patients and 18 healthy control subjects. The results identified 123 genes as critical for COVID-19 progression from 60,683 candidate probes, including immune-related genes. The 123 genes were enriched in binding sites for transcription factors NFKB1 and RELA, which are involved in various biological phenomena such as immune response and cell survival: the primary mediator of canonical nuclear factor-kappa B (NF-κB) activity is the heterodimer RelA-p50. The genes were also enriched in histone modification H3K36me3, and they largely overlapped the target genes of NFKB1 and RELA. We found that the overlapping genes were downregulated in COVID-19 patients. These results suggest that canonical NF-κB activity was suppressed by H3K36me3 in COVID-19 patient blood.

Heuristic Breadth-First Search Algorithm for Informative Gene Selection Based on Gene Expression Profiles

2009 ◽  
Vol 31 (4) ◽  
pp. 636-649 ◽  
Author(s):  
Shu-Lin WANG ◽  
Ji WANG ◽  
Huo-Wang CHEN ◽  
Shu-Tao LI ◽  
Bo-Yun ZHANG
Keyword(s):  
Gene Expression ◽  
Gene Selection ◽  
Search Algorithm ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Informative Gene ◽  
Breadth First Search
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