Joint co-clustering: Co-clustering of genomic and clinical bioimaging data

摘要: For better understanding the genetic mechanisms underlying clinical observations, and defining a group of potential candidates for protein-family-inhibiting therapy, it is interesting to determine correlations between genomic, data coming from high resolution fluorescent microscopy. We introduce computational method, called joint co-clustering, that can find co-clusters or groups genes, bioimaging parameters traits are believed be closely related each other based on given empirical information. As parameters, we quantify expression growth factor receptor EGFR/erb-B family in non-small cell lung carcinoma (NSCLC) through fully-automated computer-aided analysis approach. This immunohistochemical usually performed by pathologists via visual inspection tissue samples images. Our techniques streamlines this error-prone time-consuming process, thereby facilitating diagnosis. Experimental results several real-life datasets demonstrate quantitative precision our The co-clustering method was tested with identified statistically significant protein traits. validation literature suggest proposed provide biologically meaningful genes very promising approach analyse large-scale biological study multi-factorial pathologies their alterations.