Table 7

Performance benchmark with the SL dataset under parameters that produced the highest Matthews Correlation Coefficient (MCC).

Method

threshold

sensitivity

specificity

MCC

PE


DISOPRED2

0.05

0.645

0.897

0.567

0.541

IUPred long

0.48

0.627

0.907

0.564

0.534

IUPred short

0.41

0.649

0.877

0.546

0.526

CAST

24

0.578

0.908

0.522

0.485

SEG45

3.45;3.75

0.582

0.841

0.442

0.423

DisEMBL Rem465

1

0.348

0.969

0.418

0.317

SEG25

3.05;3.35

0.460

0.885

0.387

0.345

DisEMBL Coils

1.8

0.515

0.835

0.373

0.350

SEG12

2.35;2.65

0.282

0.943

0.308

0.225

DisEMBL Hotloops

2.3

0.306

0.928

0.304

0.233


Predictors were run under parameters that produced the highest MCC over the SL dataset to benchmark their maximally possible performance. Rankings by MCC and PE differ only slightly. Interestingly, the identified optimal parameters (in regard to MCC performance over our dataset) often differed from the default parameters of the respective programs, except for DISOPRED2.

Sirota et al. BMC Genomics 2010 11(Suppl 1):S15   doi:10.1186/1471-2164-11-S1-S15

Open Data