scholarly journals Classification of Micro Array Gene Expression Data using Statistical Analysis Approach with Personalized Fuzzy Inference System

2011 ◽  
Vol 31 (1) ◽  
pp. 5-12
Author(s):  
Tamilselvi Madeswaran ◽  
G.M.Kadhar Nawaz
Keyword(s):  
Gene Expression ◽  
Statistical Analysis ◽  
Gene Expression Data ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Analysis Approach ◽  
Expression Data ◽  
Inference System ◽  
Micro Array

A new distributed feature selection technique for classifying gene expression data

2019 ◽  
Vol 12 (04) ◽  
pp. 1950039 ◽  
Author(s):  
Sarah M. Ayyad ◽  
Ahmed I. Saleh ◽  
Labib M. Labib
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Gene Expression Data ◽  
Fuzzy Inference ◽  
Research Area ◽  
Selection Method ◽  
Expression Data ◽  
Feature Selection Technique ◽  
Inference System ◽  
Selection Technique

Classification of gene expression data is a pivotal research area that plays a substantial role in diagnosis and prediction of diseases. Generally, feature selection is one of the extensively used techniques in data mining approaches, especially in classification. Gene expression data are usually composed of dozens of samples characterized by thousands of genes. This increases the dimensionality coupled with the existence of irrelevant and redundant features. Accordingly, the selection of informative genes (features) becomes difficult, which badly affects the gene classification accuracy. In this paper, we consider the feature selection for classifying gene expression microarray datasets. The goal is to detect the most possibly cancer-related genes in a distributed manner, which helps in effectively classifying the samples. Initially, the available huge amount of considered features are subdivided and distributed among several processors. Then, a new filter selection method based on a fuzzy inference system is applied to each subset of the dataset. Finally, all the resulted features are ranked, then a wrapper-based selection method is applied. Experimental results showed that our proposed feature selection technique performs better than other techniques since it produces lower time latency and improves classification performance.

Adaptive Neuro-Fuzzy Inference System for Classification of ACL-Ruptured Knees Using Arthrometric Data

2008 ◽  
Vol 36 (9) ◽  
pp. 1449-1457 ◽  
Author(s):  
Zoya Heydari ◽  
Farzam Farahmand ◽  
Hossein Arabalibeik ◽  
Mohamad Parnianpour
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Neuro Fuzzy

scholarly journals GENECFE-ANFIS: A NEURO-FUZZY INFERENCE SYSTEM TO INFER GENE-GENE INTERACTIONS BASED ON RECOGNITION OF MICROARRAY GENE EXPRESSION PATTERNS

2007 ◽  
Vol 19 (02) ◽  
pp. 71-78 ◽  
Author(s):  
Cheng-Long Chuang ◽  
Chung-Ming Chen ◽  
Grace S. Shieh ◽  
Joe-Air Jiang
Keyword(s):  
Gene Expression ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Expression Patterns ◽  
Gene Interactions ◽  
Microarray Gene Expression ◽  
Inference System ◽  
Qrt Pcr ◽  
Neuro Fuzzy ◽  
Microarray Gene

A neuro-fuzzy inference system that recognizes the expression patterns of genes in microarray gene expression (MGE) data, called GeneCFE-ANFIS, is proposed to infer gene interactions. In this study, three primary features are utilized to extract genes' expression patterns and used as inputs to the neuro-fuzzy inference system. The proposed algorithm learns expression patterns from the known genetic interactions, such as the interactions confirmed by qRT-PCR experiments or collected through text-mining technique by surveying previously published literatures, and then predicts other gene interactions according to the learned patterns. The proposed neuro-fuzzy inference system was applied to a public yeast MGE dataset. Two simulations were conducted and checked against 112 pairs of qRT-PCR confirmed gene interactions and 77 TFs (Transcriptional Factors) pairs collected from literature respectively to evaluate the performance of the proposed algorithm.

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