A novel biclustering algorithm of binary microarray data: BiBinCons and BiBinAlter

Abstract

The biclustering of microarray data has been the subject of a large research. No one of the existing biclustering algorithms is perfect. The construction of biologically significant groups of biclusters for large microarray data is still a problem that requires a continuous work. Biological validation of biclusters of microarray data is one of the most important open issues. So far, there are no general guidelines in the literature on how to validate biologically extracted biclusters. In this paper, we develop two biclustering algorithms of binary microarray data, adopting the Iterative Row and Column Clustering Combination (IRCCC) approach, called BiBinCons and BiBinAlter. However, the BiBinAlter algorithm is an improvement of BiBinCons. On the other hand, BiBinAlter differs from BiBinCons by the use of the EvalStab and IndHomog evaluation functions in addition to the CroBin one (Bioinformatics 20:1993–2003, 2004). BiBinAlter can extracts biclusters of good quality with better p-values.

A hybrid multi-objective whale optimization algorithm for analyzing microarray data based on Apache Spark

“…Table 1 presents an example of the first set of rows of the Human Fibroblasts Serum microarray dataset (Maulik, Mukhopadhyay & Bandyopadhyay, 2021). Analyzing microarray data enables researchers to obtain valuable information, such as identifying correlated genes (Chou et al, 2007), evaluating the response of cells to a specific type of treatment (Ban et al, 2011), and identifying different types of cancer (Saber & Elloumi, 2015).…”

“…It generates data that describe the expression profiles of samples during experiments. Analyzing microarray data can help researchers to discover valuable information about samples, such as identifying correlated genes (Liu, Cheng & Tseng, 2013;Chou et al, 2007), predicting patient response to specific treatments (Ban et al, 2011), and identifying different classes of cancer (Saber & Elloumi, 2015). Clustering has been an important data analysis tool (Jain, Murty & Flynn, 1999).…”

Peer Review #1 of "A hybrid multi-objective whale optimization algorithm for analyzing microarray data based on Apache Spark (v0.1)"

“…Table 1 presents an example of the first set of rows of the Human Fibroblasts Serum microarray dataset (Maulik et al (202)). Analyzing microarray data enables researchers to obtain valuable information, such as identifying correlated genes (Chou et al (2007)), evaluating the response of cells to a specific type of treatment (Ban et al (2011)), and identifying different types of cancer (Saber and Elloumi (2015) (Maulik et al (202)).…”

“…It generates data that describe the expression profiles of samples during experiments. Analyzing microarray data can help researchers to discover valuable information about samples, such as identifying correlated genes (Liu et al (2013), Chou et al (2007)), predicting patient response to specific treatments (Ban et al (2011)), and identifying different classes of cancer (Saber and Elloumi (2015)). Clustering has been an important data analysis tool (Jain et al (1999)).…”

A binary biclustering algorithm based on the adjacency difference matrix for gene expression data analysis

“…Furthermore, the BiBinAlter algorithm proposed by Saber et al . [ 10 ], the Binary Matrix Factorization (BMF) proposed by Zhang et al . [ 11 ], the QUBIC2 algorithm proposed by Xie et al .…”