Table 1

Overview of performance of several methods and measures for prediction of motif enrichment on artificial and real datasets
Method or measure Type Artificial data Real data Reference
Recall Precision F-measure Recall Precision F-measure
LocaMo Finder (Gaussian) local 0.755 0.609 0.674 0.371 0.757 0.498 this study
LocaMo Finder (uniform) local 0.727 0.519 0.606 0.343 0.723 0.465 this study
RSAT (Binomial distribution) ($) global 0.714 0.285 0.408 0.429 0.440 0.434 RSAT [40]
ORI (**) global 0.677 0.386 0.492 0.343 0.563 0.426 this study
Hypergeometric distribution (*) global 0.745 0.272 0.399 0.400 0.450 0.424 AlignACE [41]
Fisher’s exact test (*) global 0.747 0.276 0.403 0.400 0.443 0.420 oPOSSUM [42]
ORI (*) global 0.768 0.258 0.387 0.429 0.407 0.417 this study
RSAT (Binomial distribution) ($$) global 0.591 0.498 0.541 0.271 0.607 0.375 RSAT [40]
Hypergeometric distribution (***) global 0.605 0.522 0.560 0.243 0.706 0.361 AlignACE [41]
Fisher’s exact test (***) global 0.605 0.530 0.565 0.243 0.667 0.356 oPOSSUM [42]
Casimiro et al. local 0.727 0.053 0.099 0.629 0.132 0.218 [9]
Berendzen et al. local 0.859 0.044 0.083 0.786 0.093 0.167 [1]
Vardhanabhuti et al. local 0.409 0.079 0.133 0.314 0.090 0.139 [3]
FIRE (Information content) global 0.586 0.342 0.432 0.100 0.200 0.133 FIRE [43]
TFM-Explorer local 0.432 0.145 0.217 0.186 0.076 0.108 [6]
FREE local 0.155 0.182 0.167 0.029 0.013 0.018 [5]
A-GLAM local 0.032 0.259 0.057 0.000 0.000 NA [4,27]

For each method or measure the type of measure (“local”: local enrichment of positioning; “global”: global enrichment), the recall, precision, and F-measure is given for the artificial and real datasets, as well as a reference. Methods are sorted by decreasing F-measure obtained on the real datasets. (*) P value threshold 0.01; (**) P value threshold 0.001; (***) P value threshold 1e-4; ($) sig threshold 0; ($$) sig threshold 2.

Vandenbon et al.

Vandenbon et al. BMC Bioinformatics 2013 14:26   doi:10.1186/1471-2105-14-26

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