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83 results for “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.
Phosphorylation Regulation Mechanism of β2 Integrin for the Binding of Filamin Revealed by Markov State Model
<p>Datasets of MD and MSM trajectories</p>
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: A nonstationary Markov model detects directional evolution in hymenopteran morphology
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Data from: Low-parameter phylogenetic inference under the general Markov model
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Data from: Tibial nerve decompression for the prevention of the diabetic foot: a cost-utility analysis using Markov model simulations.
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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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MESA files for paper "Progenitor properties of type II supernovae: fitting to hydrodynamical models using Markov chain Monte Carlo methods"
<p>Inlists to reproduce the pre-SN simulations of the paper "Progenitor properties of type II supernovae: fitting to hydrodynamical models using Markov chain Monte Carlo methods". These simulations were performed using MESA version 10398.</p>
Supplementary data for: "Progenitor properties of type II supernovae: fitting to hydrodynamical models using Markov chain Monte Carlo methods"
<p>This entry contains a grid of bolometric light curve and photospheric velocity models applied to stellar evolution progenitors. A full description of the models can be found in Martinez et al. 2020, A&A, 642, A143.</p>
Figure 4 from: Tachkov K, Mitov K, Savova A (2019) Predicting the outcomes and costs for a cohort of 426 patients with Chronic Obstructive Pulmonary Disease (COPD) in Bulgaria through a Markov model. Pharmacia 66(2): 53-57. https://doi.org/10.3897/pharmacia.66.e35162
Figure 4 Tornado diagram for LYS
Figure 2 from: Tachkov K, Mitov K, Savova A (2019) Predicting the outcomes and costs for a cohort of 426 patients with Chronic Obstructive Pulmonary Disease (COPD) in Bulgaria through a Markov model. Pharmacia 66(2): 53-57. https://doi.org/10.3897/pharmacia.66.e35162
Figure 2 CEAC of all data points
Figure 1 from: Tachkov K, Mitov K, Savova A (2019) Predicting the outcomes and costs for a cohort of 426 patients with Chronic Obstructive Pulmonary Disease (COPD) in Bulgaria through a Markov model. Pharmacia 66(2): 53-57. https://doi.org/10.3897/pharmacia.66.e35162
Figure 1 ICER points and dispersion cloud of Monte-Carlo simulation
Figure 3 from: Tachkov K, Mitov K, Savova A (2019) Predicting the outcomes and costs for a cohort of 426 patients with Chronic Obstructive Pulmonary Disease (COPD) in Bulgaria through a Markov model. Pharmacia 66(2): 53-57. https://doi.org/10.3897/pharmacia.66.e35162
Figure 3 Tornado diagram for QALYs
Verification of Multi-Objective Markov Models: Replication Package
<p>Contains benchmarks, execution scripts, log files, and the Storm version exercised for the experiments given in Chapter 9 of the work <strong>Verification of Multi-Objective Markov Models</strong>.</p> <p>List of contents:</p> <ul> <li>storm-package.zip: contains the Storm version considered in the experiments including installation instructions and scripts. Tested on the <a href="https://doi.org/10.5281/zenodo.7113222">TACAS'23 artefact evaluation VM</a></li> <li>totalrew.zip: contains the benchmarks and experimental data for the evaluation of expected total reward computations (Sections 9.2)</li> <li>multi.zip: contains the benchmarks and experimental data for the evaluation of multi-objective verification (Sections 9.3 to 9.6)</li> </ul> <p> </p>
Molecular Markers in Cervical Cancer Screening in the Feasibility of the Mathematical Markov Model Analysis
ClinicalTrials.gov study NCT00889902. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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.
Using Markov Models of Fault Growth Physics and Environmental Stresses to Optimize Control Actions
A contrived example of a dice throwing game was considered in order to provide some insight into the general problem developing prognostics-based control routines that utilize uncertain models of component fault dynamics and future environmental stresses to assess and mitigate risk. A generalized Markov modeling representation of fault dynamics was developed for the case that available modeling of fault growth physics and available modeling of future environ- mental stresses are represented by two independent Markov process models. A finite horizon dynamic programming algorithm was given for a Markov decision process representation of the prognostics-based control problem and this algorithm was used to identify an optimal control policy for the dice throwing game that is considered in this paper. The outcomes obtained from simulations of the optimizing control policy were observed to differ only slightly from the outcomes that would have been achievable if all modeling uncertainties were removed from the example dice throwing game.
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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.