Therapeutic indications and drug side-effects are both measureable human behavioral or physiological changes in response to the treatment. In modern drug development, both inferring potential therapeutic indications and identifying clinically important drug side-effects are challenging tasks. Previous studies have utilized either chemical structures or protein targets to predict indications and side-effects. In this study, we compared indication prediction using side-effect information and side-effect prediction using indication information against models using only chemical structures and protein targets. Experimental results based on 10-fold cross-validation, show that drug side-effects and therapeutic indications are the most predictive features for each other. In addition, we extracted 6,706 statistically highly correlated disease-side-effect pairs from all known drug-disease and drug-side-effect relationships. Many relationship pairs provide explicit repositioning hypotheses (e.g., drugs causing postural hypotension are potential candidates for hypertension) and clear adverse-reaction watch lists (e.g., drugs for heart failure possibly cause impotence). All data sets and highly correlated disease-side-effect relationships are available at http://astro.temple.edu/~tua87106/druganalysis.html.