Text Mining Approach to Analyse the Relation between Obesity and Breast Cancer Data

Abstract

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.

Identifying gene‐environment interactions incorporating prior information

“…Multiple software tools are available for such a purpose, including VxInsight, MedMiner, UALCAN, PubMatrix, and others. In our data analysis, we adopt PubMatrix (http://pumatrix.grc.nia.nih.gov/), which is web‐based literature mining tool and has been used in the studies of Hansen et al, Minafra et al, Kumar et al, and many others. More specifically, PubMatrix can conduct a two‐dimensional search using any two lists of keywords.…”