Eating disorders are central reason of physical and psycho-social morbidity. Several factors have been identified as being associated with the prevalence and progression of eating disorders in humans. Scientific investigation was carried out to assess the usage of terms in manuscript titles of nearly 900 published articles followed by network analysis and network centralities using R programming. The tm package, term document matrix function was utilized to create a term document matrix (TDM) from the corpus. A binary word matrix comprising 17 terms was created based on higher probability of occurring a term in a column. An agglomerative hierarchical clustering technique using ward clustering algorithm was presented. A data frame from the TDM was created to store data and used to plot word cloud based on word frequencies. An undirected network graph was plotted based on terms that appeared in the term matrix. Centralization measures such as Degree centrality, Closeness, Eigenvector and betweenness Centrality were reported.
In linear regression analysis, when data was derived from various reference sources, the experimental quality of such data has to be assessed. Significant variables based on the statistical data of analysis were chosen. Based on the parameters like correlation coefficient (r), F-value, cross-validation r2 etc quality of the generated equation was judged. An additional condition for high predictive ability of regression model is based on external set cross-validation r2, (R2
cv,ext) and the regression of observed activities against predicted activities and vice versa for validation set. Multivariate regression analysis using python program resulted in few influential parameters displayed significant positive and negative contribution towards biological activity of COX-2 inhibitors. A new regression model was attempted by dividing the complete set (n=64) as a 58 molecule training set and a 6 molecule validation set based on selection criteria after rejecting outliers from the data set.
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