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Results Extended Evaluation and Corpus Creation Beyond the cross-validation evaluation described above, we have previously applied our method to 12,557 previously unseen JCN abstracts (those not in our corpus) and compared a standardized subset of 2,688 relationships to the data in BAMS (Bota et al., 2012). We found that 63.5% of these connections were reported in BAMS. Using the BAMS data as a gold standard, we also found that precision can be increased at the cost of recall by requiring connections Aldosterone to occur more than once across the corpus (French et al., 2012). To extend these results and obtain more training data, we have now created a new corpus by extending our previous evaluation of 2000 positive predictions (French et al., 2012). Figure ?Figure22 outlines the creation of new corpora from the original corpus. This new corpus is based on running our framework on the test set of 12,557 JCN abstracts. Most importantly, MLN8237 manufacturer to gauge recall we had to identify negative examples, as our previous effort only manually evaluated positive predictions. By adding new evaluations of negative predictions, the new corpus contains 11,825 brain region pairings extracted from the 12,557 abstracts (12% of possible within sentence pairings), of which 18% were considered positive examples. Recall was 45.5% (as previously reported on the 2000 positive predictions, precision is 55.3%). The drop in accuracy compared to the previous cross-validation test appears to be partly due to automation of preprocessing steps that were done manually in the original corpus of 1,377 abstracts. These automated steps are imperfect and thus a source of errors upstream C59 ic50 of the connection prediction step. Specifically, we found that many classification errors could be ascribed to problems with brain region mention extraction (~10�C15% of errors) and abbreviation expansion (