{"ref":"master","ref_type":"branch","master_branch":"master","description":"Bioinformatics data analysis project concerning two data tables (adverse drug rxns per drug, and gene expression data per drug). Here we find drugs that are correlated (or similar in effect) based on gene expression data.  Forward stepwise and backward elimination logistic regression methods are used to find which adverse drug rxns can be predicted with the smallest error and AIC, as well as obtaining their respective predictive models.  Forward stepwise selection begins with the empty model and greedily adds features (genes) based on smallest error gain at each iteration.  Backward elimination begins with a full model and removes features based on greatest reduction in error.","pusher_type":"user"}
{"actor":{"display_login":"aarondevlin"}}