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Summary of the differences between each algorithm and baseline methods on the 112 kinase targets.
The proposed method achieves comparable results to the state-of-the-art methods on the UTKinect-Action3D dataset and achieves superior performance in comparison to baseline methods on the MSR-Action3D dataset.
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Figure 5 shows the results of performance comparison between our method and the baseline method on the test set.
Figure 11 Comparison of algorithms to baseline methods on kinome data.
For all options in the sensitivity analysis the ranks of the different baseline methods based on the summed weighted MCA scoring remained similar compared to the initial results.
In Table 1, processing results are given for the proposed, and the baseline methods obtained on the "Barbara" test image for three different values of σ.
On the whole kinome data, we compared a multi-task algorithm to a baseline method on all 112 kinase targets and recorded the number of significant differences.
We compared CTF with several existing tools as well as the PWM baseline method on a dataset generated by ChIP-seq experiments (TFBSs of 13 transcription factors in mouse genome).
The methods based on the SFF model (the first block of the table) outperform the baseline method (the Viterbi algorithm on the three-state model), reducing the error rate by 15 30%.
The new algorithm performs on-par with baseline methods in the task of Bayesian model averaging.
Experimental results show that our approach outperforms both baseline methods and state-of-the-art methods on various evaluation metrics.
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