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The hidden Markov model (HMM), initially introduced in the late 1960s, is a powerful statistical modeling tool widely adopted in such areas as signal processing, speech recognition, and time series analysis [ 29].
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The HMM is initially trained to recognise the audio data, and for animation the output for each state is replaced by the corresponding distribution of visual parameters.
As a query we again used the PFAM hmm model initially.
Specifically, Huang et al. [12] propose a 'class transition penalty' approach, where HMMs are initially employed for detecting the semantic classes of concern using multi-modal information and a product fusion operator.
Given that the majority of genes identified by EBSeq, DESeq2, edgeR and voom are also identified by EBSeq-HMM, we focus initially on genes that are identified exclusively by EBSeq-HMM.
Initially, an HMM was trained on the five HMM consensuses.
"Hmm hmm".
LaDue: "Hmm, hmm".
Elsewhere: hmm.
Q. Hmm.
CR: Hmm.
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