## Table 1 |
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Simulation Example 1 |
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False patterns |
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Methods |
X_{50} |
X_{150}X_{250} |
X_{251}X_{252} |
(Variables) |

pLPS | 94 (100) | 99 (99,99) | 96 (97,97) | 153 |

Logic | 100 (100) | 70 (88,91) | 65 (84,90) | 190 |

RF | NA (100) | NA (96,97) | NA (94,96) | (517) |

SPLR | 100 (100) | 97 (100,97) | 91 (100,98) | 712 |

*n *= 700 and *p *= 400, with no correlations. Tabulated numbers show the number of tests (out of 100)
in which the pattern was detected by each algorithm. The number outside the parentheses
is the number of times the given pattern was selected; the numbers inside the parentheses
shows how many times the variables in the pattern are detected in the model, as a
main effect or in some interaction. The final column shows the total number of times
(in 100 tests) that the algorithms selected patterns (variables for RF) that are not
in the true model.

Shi * et al.*

Shi * et al.* *BMC Bioinformatics* 2012 **13**:98 doi:10.1186/1471-2105-13-98