Method FCMSPimpute for Estimation of Missing Values in Microarray Data

Shekofeh Yaraghi and Mohammad Davarpanah Jazi

ABSTRACT
Gene expression microarray experiments produce datasets with numerous missing expression value, which can significantly affect the performance of statistical and machine learning algorithms. In this paper, we proposed a novel method based on the fuzzy clustering and the shortest path algorithm for measuring the semantic similarity on GO to estimate missing value to microarray gene expression. In this proposed method, missing values are imputed with values generated from cluster centers. Genes similarity in clustering process resolute based on both the GO structure information and, s property. We have applied the proposed method on two datasets with different percentages of missing values. The experimental results indicate that proposed method provides a higher accuracy of missing value estimation because the semantic similarity obtained by sp algorithm better correlates with the expression similarity than other node-based methods.

KEYWORDS

Missing Value; Semantic Similarity; Fuzzy Clustering; Microarray

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