scholarly journals Discovering the Relationship between Heat-Stress Gene Expression and Gene SNPs Features using Rough Set Theory

2020 ◽  
Vol 11 (2) ◽  
Author(s):  
Heba Zaki ◽  
Mohammad Nassef ◽  
Amr Ahmed ◽  
Ahmed Farouk
Keyword(s):  
Gene Expression ◽  
Heat Stress ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
The Relationship ◽  
Stress Gene

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 A COMPARISON BETWEEN MRA AND ROUGH SET THEORY FOR MASS APPRAISAL. A CASE IN BARI

2004 ◽  
Vol 8 (4) ◽  
pp. 205-217 ◽  
Author(s):  
Maurizio D'amato
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Property Market ◽  
Specific Group ◽  
Market Data ◽  
Property Valuation ◽  
A Value ◽  
Valuation Methodology ◽  
The Relationship

Rough Set Theory is a property valuation methodology recently applied to property market data (d'Amato, 2002). This methodology may be applied in property market where few market data are available or where econometric analysis may be difficult or unreliable. This methodology was introduced by a polish mathematician (Pawlak, 1982). The model permit to estimate a property without defining an econometric model, although do not give any estimation of marginal or hedonic prices. I : ,he first version of RST was necessary to organize the data in classes before the valuation .The relationship between these classes defined if‐then rules. If a property belongs to a specific group then it will belong to a class of value. The relationship between the property and the class of value is dichotomous. In this paper will be offered a second version that improve the RST with a “value tolerance relation” in order to make more flexible the rule. In this case the results will come out from an explicit and specific relationship. The methodology has been tested on 69 transactions in the zone of Carrassi-Poggiofranco in the residential property market of Bari.

COMBINATION ENTROPY AND COMBINATION GRANULATION IN ROUGH SET THEORY

2008 ◽  
Vol 16 (02) ◽  
pp. 179-193 ◽  
Author(s):  
YUHUA QIAN ◽  
JIYE LIANG
Keyword(s):  
Mutual Information ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Information Gain ◽  
Heuristic Function ◽  
Nature Of Information ◽  
Knowledge Content ◽  
Incomplete Information Systems ◽  
The Relationship

Based on the intuitionistic knowledge content nature of information gain, the concepts of combination entropy and combination granulation are introduced in rough set theory. The conditional combination entropy and the mutual information are defined and their several useful properties are derived. Furthermore, the relationship between the combination entropy and the combination granulation is established, which can be expressed as CE(R) + CG(R) = 1. All properties of the above concepts are all special instances of those of the concepts in incomplete information systems. These results have a wide variety of applications, such as measuring knowledge content, measuring the significance of an attribute, constructing decision trees and building a heuristic function in a heuristic reduct algorithm in rough set theory.

Comparative Analysis of Hybrid Soft Set Methods

2014 ◽  
pp. 360-383
Author(s):  
Debadutta Mohanty
Keyword(s):  
Information System ◽  
Comparative Analysis ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Soft Set ◽  
Mathematical Tool ◽  
Soft Rough Set ◽  
Definition Of ◽  
The Relationship

The whole mathematical scenario has changed with the advent of the Rough Set Theory, a powerful tool to deal with uncertainty and incompleteness of knowledge in information system. With the advancement of research, the Soft Set Theory has emerged as an advanced mathematical tool to deal with data associated with uncertainty. The present chapter endeavors to forge a connection between soft set and rough set and maps a new model rough soft set to address the challenges of vagueness and impreciseness. Although the research contribution of M. Irfan Ali, Dan Meng, et al. and Feng Feng et al. had given distinct definition of rough soft set and soft rough set, the analysis explaining the genesis of these sets is not appropriate. This chapter is a new attempt to construct the relationship between a rough set, soft set, and fuzzy set to form a hybrid soft set giving a concrete comprehensive definition of rough soft set in border perspective.

Uncertainty Measures for Multigranulation Approximation Space

2015 ◽  
Vol 23 (03) ◽  
pp. 443-457 ◽  
Author(s):  
Guoping Lin ◽  
Jiye Liang ◽  
Yuhua Qian
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Information Entropy ◽  
Rough Set Theory ◽  
Mathematical Tool ◽  
Approximation Space ◽  
Uncertainty Measures ◽  
Multigranulation Rough Set ◽  
Essential Properties ◽  
The Relationship

Multigranulation rough set theory is a relatively new mathematical tool for solving complex problems in the multigranulation or distributed circumstances which are characterized by vagueness and uncertainty. In this paper, we first introduce the multigranulation approximation space. According to the idea of fusing uncertain, imprecise information, we then present three uncertainty measures: fusing information entropy, fusing rough entropy, and fusing knowledge granulation in the multigranulation approximation space. Furthermore, several essential properties (equivalence, maximum, minimum) are examined and the relationship between the fusion information entropy and the fusion rough entropy is also established. Finally, we prove these three measures are monotonously increasing as the partitions become finer. These results will be helpful for understanding the essence of uncertainty measures in multigranulation rough space and enriching multigranulation rough set theory.

scholarly journals Rough ACO: A Hybridized Model for Feature Selection in Gene Expression Data

2010 ◽  
pp. 85-98
Author(s):  
Debahuti Mishra ◽  
Dr. Amiya Kumar Rath ◽  
Dr. Milu Acharya ◽  
Tanushree Jena
Keyword(s):  
Gene Expression ◽  
Dimensionality Reduction ◽  
Set Theory ◽  
Gene Expression Data ◽  
Rough Set ◽  
Rough Set Theory ◽  
Optimality Criteria ◽  
Expression Data ◽  
Essential Information ◽  
Novel Method

Dimensionality reduction of a feature set is a common preprocessing step used for pattern recognition, classification applications and in compression schemes. Rough Set Theory is one of the popular methods used, and can be shown to be optimal using different optimality criteria. This paper proposes a novel method for dimensionality reduction of a feature set by choosing a subset of the original features that contains most of the essential information, using the same criteria as the ACO hybridized with Rough Set Theory. We call this method Rough ACO. The proposed method is successfully applied for choosing the best feature combinations and then applying the Upper and Lower Approximations to find the reduced set of features from a gene expression data.

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