In addition to identifying biases in genome-wide evaluations of datasets or methods, our evaluation framework provides a way to normalize these biases out of the analysis.
Employing published solubility data as well as data estimated using the UNIFAC method, our systematic evaluation identifies ethanol (EtOH) and ethyl acetate (EtOAc) as promising candidate antisolvents for continuous crystallisation.
Information theoretic methods are frequently used to predict SDPs, so we include several such methods in our evaluation.
Combining quantitative and qualitative methods in our evaluation of a complex HIE intervention has two potential benefits: complementarity and multiplicativeness, i.e., the methods may illuminate different aspects of the intervention impacts and the combination of the two methods may provide greater insight than either method individually [ 35].
For that purpose we used MET as a second method, but in our evaluation, this method showed low sensitivity (58.3%) but good specificity (92.1%) similar to recent studies.
This method differed from the other methods by, in our evaluation, assigning studies the highest reliability category (see the Summary of the reliability evaluation of the non-standard test data section).
The evaluation method adopted in our evaluation is called active evaluation, in which we execute real botnet binaries in a controlled network environment.
Although a comparison is not possible due to the different evaluation method, our results are similar to those achieved in the IDEAL 1 study, in which symptomatic improvement occurred in 40.3% of patients treated with the 250 mg gefitinib daily dose.
Because of the time-consuming training required for supervised methods, we limited our evaluation to networks with 30 nodes extracted from E. coli networks.
Four IC based methods, including funSimMax and the relevance method, are selected in our evaluation.
