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A second class of methods uses multiple parameters per event (per voxel) to capture temporal information more faithfully.
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Some methods used multiple cameras to reconstruct backgrounds in PGS using the plane sweep algorithm [29, 40].
Two previous studies have attempted to recover abdominal contents through systematic sampling methods using multiple inhumations and two others have recovered colon contents in isolated instances.
Previous view-invariant methods use multiple cameras in both the training and testing phases of action recognition or require storing many examples of a single action from multiple viewpoints.
Drop-out rate was 12%, comparable to traditional sequencing methods using multiple markers [ 45].
We perform an extensive simulation study and compare the trees from all methods using multiple criteria.
Methods using multiple genomic data sources have also been proposed [ 9, 10].
Multiple-kernel learning methods use multiple kernels by combining them into a single one via a combination function.
In addition, the methods using multiple data sources are generally better than their counterparts using one single type of data.
Below we benchmark the performance of PHLAT together with other methods using multiple public and in-house datasets.
To enable comparisons between functional similarity methods using multiple annotation sources and those using only GO annotations, proteins with no available GO annotation were excluded.
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