ஜர்னல் ஆஃப் புரோட்டியோமிக்ஸ் & பயோ இன்ஃபர்மேடிக்ஸ்
திறந்த அணுகல்

சுருக்கம்

Efficiently Mining Gene Expression Data via Novel Binary Biclustering Algorithms

Haifa Ben Saber and Mourad Elloumi

In this paper, we present four new algorithms called, BiBin Alter, BiBin Cons and BiBin Sim, for biclustering of binary microarray data. There are novel alternatives to extract biclusters from sparse binary datasets. Our algorithms are based on Iterative Row and Column Clustering Combination (IRCCC) and Divide and Conquer (DC) approaches, Bi Max initialization and the Cro Bin evaluation function. Applied on binary synthetic datasets, our algorithms outperform other biclustering algorithms for binary microarray data. Biclusters with different numbers of rows and columns can be detected, varying from many rows to few columns and few rows to many columns. Our algorithms allow the user to guide the search towards biclusters of specific dimensions.