Results 11 to 20 of about 43,679 (257)
Clustering metagenomic sequences with interpolated Markov models [PDF]
BMC Bioinformatics, 2010 Background Sequencing of environmental DNA (often called metagenomics) has shown tremendous potential to uncover the vast number of unknown microbes that cannot be cultured and sequenced by traditional methods.
Kelley David R, Salzberg Steven L
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MACHOS: Markov clusters of homologous subsequences [PDF]
Bioinformatics, 2008 Abstract Motivation: The classification of proteins into homologous groups (families) allows their structure and function to be analysed and compared in an evolutionary context. The modular nature of eukaryotic proteins presents a considerable challenge to the delineation of families, as different local regions within a single protein ...
Simon Wong, Mark A. Ragan
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Mode Clustering for Markov Jump Systems [PDF]
2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2019 In this work, we consider the problem of mode clustering in Markov jump models. This model class consists of multiple dynamical modes with a switching sequence that determines how the system switches between them over time. Under different active modes, the observations can have different characteristics. Given the observations only and without knowing
Zhe Du, Necmiye Ozay, Laura Balzano
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Markov Chains and Spectral Clustering [PDF]
, 2011 The importance of Markov chains in modeling diverse systems, including biological, physical, social and economic systems, has long been known and is well documented. More recently, Markov chains have proven to be effective when applied to internet search engines such as Google’s PageRank model [7], and in data mining applications wherein data trends ...
Ning Liu, William J. Stewart 0001
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Bayesian clustering for continuous‐time hidden Markov models [PDF]
Canadian Journal of Statistics, 2021 We develop clustering procedures for longitudinal trajectories based on a continuous‐time hidden Markov model (CTHMM) and a generalized linear observation model. Specifically, in this article we carry out finite and infinite mixture model‐based clustering for a CTHMM and achieve inference using Markov chain Monte Carlo (MCMC).
Yu Luo +2 more
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Clustering Markov Decision Processes For Continual Transfer [PDF]
CoRR, 2013 56 pages, Working ...
Mahmud, M. M. +3 more
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Markov properties of cluster processes [PDF]
Advances in Applied Probability, 1996 We show that a Poisson cluster point process is a nearest-neighbour Markov point process [2] if the clusters have uniformly bounded diameter. It is typically not a finite-range Markov point process in the sense of Ripley and Kelly [12]. Furthermore, when the parent Poisson process is replaced by a Markov or nearest-neighbour Markov point process, the ...
A.J. Baddeley (Adrian) +2 more
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Deconvolutive Clustering of Markov States [PDF]
, 2006 In this paper we formulate the problem of grouping the states of a discrete Markov chain of arbitrary order simultaneously with deconvolving its transition probabilities. As the name indicates, this problem is related to deconvolutive blind signal separation.
Ata Kabán, Xin Wang 0006
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The Metagenomic Binning Problem: Clustering Markov Sequences [PDF]
2019 IEEE Information Theory Workshop (ITW), 2019 The goal of metagenomics is to study the composition of microbial communities, typically using high-throughput shotgun sequencing. In the metagenomic binning problem, we observe random substrings (called contigs) from a mixture of genomes and want to cluster them according to their genome of origin.
Grant Greenberg, Ilan Shomorony
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Guided Cluster Discovery with Markov Model [PDF]
Applied Intelligence, 2005 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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