Using Adaptive Neuro-Fuzzy Inference System for Classification of Microarray Gene Expression Cancer Profiles | Zendy

Bülent  Haznedar | Zendy; Mustafa Turan Arslan | Zendy; Adem  Kalinli | Zendy

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Using Adaptive Neuro-Fuzzy Inference System for Classification of Microarray Gene Expression Cancer Profiles

Author(s) -

Bülent Haznedar,

Mustafa Turan Arslan,

Adem Kalinli

Publication year - 2018

Publication title -

tamap journal of engineering

Language(s) - English

Resource type - Journals

ISSN - 2602-2362

DOI - 10.29371/2018.3.29

Subject(s) - adaptive neuro fuzzy inference system , artificial intelligence , computer science , inference system , dna microarray , inference , support vector machine , microarray analysis techniques , machine learning , pattern recognition (psychology) , bayes' theorem , fuzzy logic , data mining , microarray , expression (computer science) , gene , bayesian probability , gene expression , fuzzy control system , biology , genetics , programming language

Microarray is a technology that enables simultaneously analysis of thousands of genes in DNA structure depending on the advances in biochemistry. With this technology, it has become possible to diagnose and treat heredity diseases by analyzing thousands of gene expression levels. This study proposes an artificial intelligence method, Adaptive neuro-fuzzy inference system (ANFIS), to classify cancer gene expression profiles. The findings obtained with the proposed ANFIS approach are compared with the results of statistical methods such as Naive Bayes and Support Vector Machines. In conclusion, although the highest average classification performance achieved with ANFIS is 95.56%, the highest performance achieved with statistical methods are found to be 87.65%.

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