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Although HMM aims to minimize barriers to care, there are potential downsides to this strategy [5].
Outcome 3 suggests that although HMM is more effective, resources would be better used elsewhere.
However, although HMM used in CNAG gives a reasonable inference of copy numbers, it does not treat the log-2 ratio in a local region as a pattern, some individual SNPs are reported as abnormal ones, which are not reliable.
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Although HMMs exploit this temporal dependency, the features fed to these HMMs can be extracted in the frequency domain.
Although HMM-based methods have been developed relatively recently, they offer significant improvements in computational speed, especially for sequences that contain overlapping regions.
Although HMMs have been proposed for disease surveillance [ 4, 5], they appear to have been rarely used in practice.
Although HMMs have become established in movement recognition and even generation [ 30], they are bounded to some methodological restrictions, which arise especially in social interaction between humans and artificial agents.
Although iterated HMM is based on phyla, this taxonomic unit is sometimes too diverse to accurately follow phylogeny with recursive means.
Although learning HMM-As can be more complex, it is shown that it is feasible in practice due to their ability to maintain compact state spaces, yet more expressive ones.
Although L-HMMs are advantageous in terms of robustness to under-training occurrences compared to H-HMMs, this attribute is accompanied by reduced efficiency in modeling long-term temporal relationships in the data.
Although the HMMs are imperfect, if two homologous regions are misclassified into different families by one source of models, they will usually be classified as homologs by a model from another source or by one of the additional families that FastBLAST creates.
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