Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí y học Molecular Biology cung cấp cho các bạn kiến thức về ngành sinh học đề tài: Differential co-expression framework to quantify goodness of biclusters and compare biclustering algorithms. | Chia and Karuturi Algorithms for Molecular Biology 2010 5 23 http content 5 1 23 AMR ALGORITHMS FOR ID MOLECULAR BIOLOGY RESEARCH Open Access Differential co-expression framework to quantify goodness of biclusters and compare biclustering algorithms Burton Kuan Hui Chia1 3 and R Krishna Murthy Karuturi 2 Abstract Background Biclustering is an important analysis procedure to understand the biological mechanisms from microarray gene expression data. Several algorithms have been proposed to identify biclusters but very little effort was made to compare the performance of different algorithms on real datasets and combine the resultant biclusters into one unified ranking. Results In this paper we propose differential co-expression framework and a differential co-expression scoring function to objectively quantify quality or goodness of a bicluster of genes based on the observation that genes in a bicluster are co-expressed in the conditions belonged to the bicluster and not co-expressed in the other conditions. Furthermore we propose a scoring function to stratify biclusters into three types of co-expression. We used the proposed scoring functions to understand the performance and behavior of the four well established biclustering algorithms on six real datasets from different domains by combining their output into one unified ranking. Conclusions Differential co-expression framework is useful to provide quantitative and objective assessment of the goodness of biclusters of co-expressed genes and performance of biclustering algorithms in identifying co-expression biclusters. It also helps to combine the biclusters output by different algorithms into one unified ranking . metabiclustering. Background The inception of microarrays has facilitated quantification of expression of genes at genomic scale in large sets of conditions in time and cost effective manner resulting in a wealth of massive gene expression datasets. Appropriate analysis of these .
