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The latter allows the model to cluster the current data well and to be consistent with the recent history.
In this research we propose first a model to cluster SNPs data, then we assess the association between the cluster and a disease.
Wang et al. [1] use a hierarchical Bayesian model to cluster activities and interactions in surveillance videos.
First it uses the localized haplotype cluster model to cluster haplotypes at each marker and then defines an HMM to find the most likely haplotype pairs based on the individual's known genotypes.
We have adapted the model to cluster randomized trials.
We input the entire component matrix corresponding to the locus link mode for a 12-factor PARAFAC model to cluster locus links.
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To combat the problem, this paper proposes an efficient automatic method by extending the decision-theoretic rough set model to clustering.
MCL then applies graph theory and hidden Markov models to cluster sets of proteins based on their proximity in that space8.
Beagle uses a localized haplotype cluster-model to cluster haplotypes at each marker and then defines a HMM to find the most likely haplotype pairs based on the individual's known genotypes.
Then we describe the machine learning algorithm that uses HMM models to cluster tumors.
To determine whether the miRNA expression changes observed in these Tg liver cancer models are relevant to human HCC, we used the shared 60-miRNA signature found in our models to cluster human HCC patient samples (Fig. 2B,C).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com