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Table 1 AUC for different models on both simulated datasets.

From: Parameter estimation for robust HMM analysis of ChIP-chip data

 

TileMap

Baum-Welch

Viterbi-Training

Viterbi-EM

ad hoc

dataset I

0.9986

0.9998

0.9997

0.9998

0.9869

dataset II

0.9749

0.9995

0.9994

0.9995

0.9728

  1. All models with optimised parameters outperform TileMap on both simulated datasets. While TileMap performs well on dataset I it is only slightly better than the model with ad hoc parameter estimates.
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BMC Bioinformatics

ISSN: 1471-2105

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