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47 results for “hidden Markov model”
Data from: Using hidden Markov models to improve quantifying physical activity in accelerometer data – a simulation study
Introduction The use of accelerometers to objectively measure physical activity (PA) has become the most preferred method of choice in recent years. Traditionally, cutpoints are used to assign impulse counts recorded by the devices to sedentary and activity ranges. Here, hidden Markov models (HMM) are used to improve the cutpoint method to achieve a more accurate identification of the sequence of modes of PA. Methods:1,000 days of labeled accelerometer data have been simulated. For the simulated data the actual sedentary behavior and activity range of each count is known. The cutpoint method is compared with HMMs based on the Poisson distribution (HMM[Pois]), the generalized Poisson distribution (HMM[GenPois]) and the Gaussian distribution (HMM[Gauss]) with regard to misclassification rate (MCR), bout detection, detection of the number of activities performed during the day and runtime. Results:The cutpoint method had a misclassification rate (MCR) of 11% followed by HMM[Pois] with 8%, HMM[GenPois] with 3% and HMM[Gauss] having the best MCR with less than 2%. HMM[Gauss] detected the correct number of bouts in 12.8% of the days, HMM[GenPois] in 16.1%, HMM[Pois] and the cutpoint method in none. HMM[GenPois] identified the correct number of activities in 61.3% of the days, whereas HMM[Gauss] only in 26.8%. HMM[Pois] did not identify the correct number at all and seemed to overestimate the number of activities. Runtime varied between 0.01 seconds (cutpoint), 2.0 minutes (HMM[Gauss]) and 14.2 minutes (HMM[GenPois]). Conclusions: Using simulated data, HMM-based methods were superior in activity classification when compared to the traditional cutpoint method and seem to be appropriate to model accelerometer data. Of the HMM-based methods, HMM[Gauss] seemed to be the most appropriate choice to assess real-life accelerometer data.
Data from: MatlabHTK: a simple interface for bioacoustic analyses using Hidden Markov models
1. Passive bioacoustic recording devices are now widely available and able to continuously record remotely located sites for extended periods, offering great potential for wildlife monitoring and management. Analysis of the huge datasets generated, in particular for specific biotic sound recognition, remains a critical bottleneck for widespread adoption of these technologies as current methods are labour intensive. 2. Several methods borrowed from speech processing frameworks, such as hidden Markov models, have been successful in analysing bioacoustic data but the software implementations can be expensive and difficult to use for non-specialists involved in wildlife conservation. To remedy this, we present a software interface to a popular speech recognition system making it possible for non-experts to implement hidden Markov models for bioacoustic signal processing. Octave/Matlab functions are used to simplify the set up and the definition of a bioacoustic signal recogniser as well as the analysis of the results. 3. We present the different functions as a workflow. To demonstrate how the package can be used we give the results of an analysis of a bioacoustic monitoring dataset to detect the nocturnal presence and behaviour of a cryptic seabird species, the common diving petrel Pelecanoides urinatrix urinatrix, from Northern New Zealand. 4. We show that the package matlabHTK can be used efficiently to reconstruct the daily patterns of colony activity in the common diving petrel.
CATH-KinFams: CATH Protein Kinase classification alignments and Hidden Markov Models
<p>CATH KinFams are protein kinase domain families classified according to functional similarity based on SDP. In this deposition we make available 2,210 KinFams sequence alignments alongside Hidden Markov Models built from them to be used with HMMER3.</p> <p>A concatenated library 'kinases_4.3-FF-seed.hmm' is also available to scan against the whole KinFams dataset.</p> <p>The Zenodo deposition contains:</p> <p>kinfams-cath-4.3-seed-alignments.tar.gz - KinFams FASTA file alignments with headers 'UniProt_ID/start-stop' i.e. A8XMX4/281-587</p> <p>kinfams-cath-4.3-seed-hmms.tar.gz - HMMs for each individual KinFam and concatenated in a HMM library.</p> <p>kinfams-cath-4.3-seed-mda-strings - Multi-Domain-Architecture string assignment for each sequence in the KinFams dataset.</p> <p>human_kinfams_af2_models_cif.tar.gz - Chopped mmCIF files containing Human Kinases AlphaFold2 Models.</p>
Data from: MatlabHTK: a simple interface for bioacoustic analyses using Hidden Markov models
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Data from: Using hidden Markov models to improve quantifying physical activity in accelerometer data – a simulation study
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Generalized hidden Markov models for phylogenetic comparative datasets
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VIPR HMM: A hidden Markov model for detecting recombination with microbial detection arrays
GEO Series GSE34490. Peribunyaviridae; Togaviridae; Alphavirus; Flavivirus; Chlorocebus aethiops; Flaviviridae. 114 samples. Type: Other.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.