Estimation of Missing Values Using Hybrid Fuzzy Clustering Mean and Majority Vote for Microarray Data
Year: 2019
Type: conference-paper
Abstract: Rapid development in microarray experiments leads toward generating a large amount of genes expressions. Handling such a massive amount of gene expressions is a challenge using traditional methods especially multiple missing expression values exist. This article proposed a novel hybrid fuzzy clustering mean and majority vote to yield better performance. The proposed method will be evaluated with other three imputation methods and trained with four publicly available microarray datasets. The experimental results show that the proposed method has promising results that able to replace the missing data as well as achieves high accuracy rates and low root mean square error values.
Source: Procedia Computer Science
Institutions: University of Technology Malaysia, King Abdulaziz University
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: diamond
Funder: Universiti Teknologi Malaysia
OpenAlex ID: W3000820103