SNMRS: An advanced measure for Co-expression network analysis.

The challenge of identifying modules in a gene interaction network is important for a better understanding of the overall network architecture. In this work, we develop a novel similarity measure called Scaling-and-Shifting Normalized Mean Residue Similarity (SNMRS), based on the existing NMRS technique [1]. SNMRS yields correlation values in the range of 0 to +1 corresponding to negative and positive dependency. To study the performance of our measure, internal validation of extracted clusters resulting from different methods is carried out. Based on the performance, we choose hierarchical clustering and apply the same using the corresponding dissimilarity (distance) values of SNMRS scores, and utilize a dynamic tree cut method for extracting dense modules. The modules are validated using a literature search, KEGG pathway analysis, and gene-ontology analyses on the genes that make up the modules. Moreover, our measure can handle absolute, shifting, scaling, and shifting-and-scaling correlations and provides better performance than several other measures in terms of cluster-validity indices. Also, SNMRS based module detection method results in interesting biologically relevant patterns from gene microarray and RNA-seq dataset. A set of crucial genes having high relevance with the ESCC are also identified.

PD_BiBIM: Biclustering-based biomarker identification in ESCC microarray data.

To promote diligent analysis of the progression of a disease, it is important to identify interesting biomarkers for the disease. Biclustering has already been established as an effective technique to help identify such biomarkers of high biological significance. Although in the recent past, a good number of biclustering techniques have been introduced, most of them fail to perform consistently across multiple domains or datasets. To choose a single biclustering technique that can help the accomplishment of such a critical task for multiple diseases with high precision is extremely difficult. Hence, in this study, we considered several biclustering techniques and accepted those techniques and their results which are found significant from enrichment perspective for subsequent analysis. Based on biclustering results, we constructed biological networks and carried out a topological, pathway and causal analysis on the modules extracted from the networks. Our multiobjective study enabled us to identify several biomarkers for esophageal squamous cell carcinoma (ESCC) such as IFNGR1, CLIC1, CDK4, and COPS5, after applying a ranking scheme.