SUBIC: A Supervised Bi-Clustering Approach for Precision Medicine

Traditional medicine typically applies one-size-fits-all treatment for the entire patient population whereas precision medicine develops tailored treatment schemes for different patient subgroups. The fact that some factors may be more significant for a specific patient subgroup motivates clinicians and medical researchers to develop new approaches to subgroup detection and analysis, which is an effective strategy to personalize treatment. In this study, we propose a novel patient subgroup detection method, called Supervised Biclustring (SUBIC) using convex optimization and apply our approach to detect patient subgroups and prioritize risk factors for hypertension (HTN) in a vulnerable demographic subgroup (African-American). Our approach not only finds patient subgroups with guidance of a clinically relevant target variable but also identifies and prioritizes risk factors by pursuing sparsity of the input variables and encouraging similarity among the input variables and between the input and target variable

BiETech : Bicluster Ensemble Techniques

Various biclustering algorithms have emerged now a days that try to deliver good biclusters from gene expression data which satisfy a particular objective function. Users are lost in finding the best out of these algorithms. Ensemble techniques come to rescue     of these users by aggregating all the solutions and providing a single solution which is more robust and stable than its constituent solutions.  In this paper, we present two different ensemble techniques for biclustering solutions. We have used classifiers in one approach and the other approach uses the concept of metaclustering for forming the consensus. Experiments in this research are performed   on synthetic and real gene expression datasets as biologists are interested in finding meaningful patterns in expression of genes.  The experiments show that both the approaches proposed in the paper show improvement over the input solutions as well as the existing bicluster ensemble techniques