Biclustering Algorithm for Embryonic Tumor Gene Expression Dataset: LAS Algorithm - Alavi Majd, Hamid; Shahsavari, Soodeh; Khodakarim, Soheila; Tabatabaei, Seyyed Mohammad; Nobakht Motlagh Ghochani, Bi bi Fatemeh (Archives of Advances in Biosciences, 2013)
An important step in considering of gene expression data is obtained groups of genes that have similarity patterns. Biclustering methods was recently introduced for discovering subsets of genes that have coherent values across a subset of conditions. The LAS algorithm relies on a heuristic randomized search to find biclusters. In this paper, we introduce biclustering LAS algorithm and then apply this procedure for real value gene expression data. In this study after normalized data, LAS performed. 31 biclusters were discovered that 26 of them were for positive gene expression values and others were for negative. Biological validity for LAS procedure in biological process, in molecular function and in cellular component were 77.96% , 62.28% and 74.39% respictively. The result of biological validation of LAS algorithm in this study had shown LAS algorithm effectively convenient in discovering good biclusters.