Figure 6

Process-specific evaluation example. A detailed understanding of which specific biological signals are present in a particular dataset is important for robust evaluation. Our evaluation framework allows users to query specific processes of interest. (a) Example of an evaluation of 7 high-throughput datasets over a set of 16 user-specified processes (GO terms). The precision-recall characteristics of each dataset-process combination were computed independently and the intensity of the corresponding square in the matrix is scaled according to the area under the precision-recall curve (AUPRC). (b) Detailed comparison of results for a single dataset, which can be accessed directly from the summary matrix. The AUPRC statistic of a particular dataset (e.g. Ito et al. two-hybrid) for each process is plotted to allow for comparison across a single dataset. (c) The actual precision-recall curve (from which the AUPRC was computed) is also easily accessible from our evaluation framework. Users can view underlying details of the AUPRC summary statistic which appears in the other three result views. (d) The AUPRC results for a single biological process across all datasets can also be obtained from an evaluation result. This allows for direct measure of which datasets are most informative for a process of interest.