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There are numerous genetic factors like MC4R (Melanocortin-4 receptor), POMC (Pro-opiomelanocortin), SIM1 (Single Minded Gene) etc. important in obesity, which can be used as biomarker. But more reliable diagnostic markers are the need for today, along with new therapeutic strategies that target specific molecules in the disease pathways. As in mouse and human genes, where mutations in one or both species are associated with some phenotypic characteristics as observed in human disease. In molecular mechanisms of development, differentiation, and disease gene expression data provide crucial insights. Up-regulation and down-regulation of selective genes can have major effects on diet-induced obesity, but there is little or no effect when animals are fed a low-fat diet. In present study we have studied the gene expression data of mouse at different theiler stages using GXD BioMart. The interacting partners and pathway of the genes that are already used as biomarker in mouse as well as in humans have been studied. A gene NPY1R (Neuropeptide Y1 receptor) was taken as common after STRING and KEGG results on the basis of biochemical pathways and interactions similar to MC4R. Our present work focuses on comparative genomics and proteomics analysis of NPY1R, which has led to identification of biomarker by comparing it with already known MC4R human and mouse biomarker. It has been concluded that both the proteins are structurally and functionally similar.

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Biomedical research needs to leverage and exploit large amount of information reported in scientific publication. Literature data collected from publications has to be managed to extract information, transforms into an understandable structure using text mining approaches. Text mining refers to the process of deriving high-quality information from text by finding relationships between entities which do not show direct associations. Therefore, as an example of this approach, we present the link between two diseases i.e. breast cancer and obesity.Obesity is known to be associated with cancer mortality, but little is known about the link between lifetime changes in BMI of obese person and cancer mortality in both males and females. In this article, literature data for obesity and breast cancer was obtained using PubMed database and then methodologies which employs groups of common genes and keywords with their frequency of occurrence in the data were used, aimed to establish relation between obesity and breast cancer visualized using Pi-charts and bar graphs. From the data analysis, we obtained 1 gene which showed the link between both the diseases and validated using statistical analysis and disease-connect web server. We also proposed 8 common higher frequency keywords which could be used for indexing while searching the literature for obesity and breast cancer in combination.