Microarray data clustering and visualization tool using self-organizing maps

Abstract

Microarray is one of the technologies used in the interdisciplinary science of BioInformatics. Its primary objective is to discover biological knowledge among genes through their expressions. Gene expressions usually come in large and multidimensional data which makes computational and statistical analyses necessary. Clustering of microarray data is one of these. Grouping similar genes together unfolds relationships of the biological properties of the genes under specific condition and, if supported by visualization, serves as good decision support for researchers. MaSOM is a software that uses Self-Organizing Maps, an Artificial Neural Network suitable both for clustering and for visualization. This tool can be used to analyze large data set by preprocessing, clustering, and visualizing two-color cDNA microarray data. It can therefore aid microarray researchers and practitioners in determining the initial properties of the data they study before proceeding to their actual experimentation onto their data.

Real-time classifier based on adaptive competitive self-organizing algorithm

“…Nowadays, an increasing trend in the quantity of data requires data analyzing methods such as clustering . In general, it is hard to define a universal notion for clustering (Everitt, Landau, & Leese, 2001) since it has a wide variety of applications in different domains such as pattern recognition (machine learning) (Anzai, 2012), data analysis (biology) (Marasigan, Dionisio, & Solano, 2015), image processing (segmentation) (Banerjee et al, 2015), and web application (Pang et al, 2015). Clustering can be defined as the process of partitioning a set of data into a set of meaningful sub-classes.…”