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 Using Variable Precision Rough Set for Selection and Classification of Biological Knowledge Integrated in DNA Gene Expression

2012 ◽  
Vol 9 (3) ◽  
pp. 1-17 ◽  
Author(s):  
D. Calvo-Dmgz ◽  
J. F. Gálvez ◽  
D. Glez-Peña ◽  
S. Gómez-Meire ◽  
F. Fdez-Riverola
Keyword(s):  
Gene Expression ◽  
Set Theory ◽  
Rough Set ◽  
Microarray Data ◽  
Rough Set Theory ◽  
Biological Knowledge ◽  
Variable Precision ◽  
Gene Sets ◽  
Proposed Model ◽  
Variable Precision Rough Set

Summary DNA microarrays have contributed to the exponential growth of genomic and experimental data in the last decade. This large amount of gene expression data has been used by researchers seeking diagnosis of diseases like cancer using machine learning methods. In turn, explicit biological knowledge about gene functions has also grown tremendously over the last decade. This work integrates explicit biological knowledge, provided as gene sets, into the classication process by means of Variable Precision Rough Set Theory (VPRS). The proposed model is able to highlight which part of the provided biological knowledge has been important for classification. This paper presents a novel model for microarray data classification which is able to incorporate prior biological knowledge in the form of gene sets. Based on this knowledge, we transform the input microarray data into supergenes, and then we apply rough set theory to select the most promising supergenes and to derive a set of easy interpretable classification rules. The proposed model is evaluated over three breast cancer microarrays datasets obtaining successful results compared to classical classification techniques. The experimental results shows that there are not significat differences between our model and classical techniques but it is able to provide a biological-interpretable explanation of how it classifies new samples.

scholarly journals Supervised Machine Learning in Electrofacies Classification: A Rough Set Theory Approach

2020 ◽  
Vol 1529 ◽  
pp. 052048
Author(s):  
Touhid Mohammad Hossain ◽  
Junzo Wataada ◽  
Maman Hermana ◽  
Izzatdin A Aziz
Keyword(s):  
Machine Learning ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Supervised Machine Learning ◽  
Theory Approach

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.

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