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171 results for “Model inference”
Inferring predator-prey interactions from camera traps: A Bayesian co-abundance modelling approach
<p><span>Predator-prey dynamics are a fundamental part of ecology, but directly studying interactions has proven difficult. The proliferation of camera trapping has enabled the collection of large datasets on wildlife, but researchers face hurdles inferring interactions from observational data. </span><span>Recent advances in </span><span>hierarchical c</span><span>o-abundance models infer species interactions while </span><span>accounting for two species' detection probabilities, shared responses to environmental covariates, and propagate uncertainty throughout the</span> <span>entire modelling process. However, current approaches remain </span><span>unsuitable for interacting species </span><span>whose natural densities differ by an order of magnitude and have contrasting detection probabilities, such as predator-prey interactions, which introduce zero-inflation and overdispersion in count histories. </span><span>Here we developed </span><span>a Bayesian hierarchical N-mixture co-abundance model that is </span><span>suitable for </span><span>inferring </span><span>predator-prey </span><span>interactions. We accounted for excessive zeros in count histories using an informed zero-inflated Poisson distribution in the abundance formula and accounted for overdispersion in count histories by including a random effect per sampling unit and sampling occasion in the detection probability formula. We demonstrate that models with these modifications outperform alternative approaches, improve model goodness-of-fit, and overcome parameter convergence failures. We highlight its utility using 20 camera trapping datasets </span><span>from 10 tropical forest landscapes in Southeast Asia and estimate four predator-prey relationships between tigers, clouded leopards, and muntjac and sambar deer. Tigers had a negative effect on muntjac abundance, providing support for top-down regulation, while clouded leopards had a positive effect on muntjac and sambar deer, likely driven by shared responses to unmodelled covariates like hunting. </span><span>This Bayesian co-abundance modelling approach to quantify predator-prey relationships </span><span>is widely applicable across species, ecosystems, and sampling approaches, and may be useful in forecasting cascading impacts following widespread predator declines. Taken together, this approach facilitates a nuanced and mechanistic understanding of food-web ecology.</span></p>
Data for m-NLP inference models using simulation and regression techniques
<p>This file contains data for the manuscript ``m-NLP inference models using simulation and regression techniques''.</p> <p>The "needle probe data.xlsx" file contains the simulation results and the fits to the simulation data. It also contains the coefficients a, b, and c for which a synthetic solution library can be constructed. Examples of the synthetic solution library are also included, named "nndlt.dat" and "nndlv.dat". The "center.out" file contains the Radial Basis Function density inference model created from the synthetic solution library.</p> <p>The NorSat-1 data can be obtained from: http://tid.uio.no/plasma/norsat/norsat1.html</p>
Gravity change data used in the paper "Insights into the magmatic feeding system of the 2021 eruption at Cumbre Vieja (La Palma, Canary Islands) inferred from gravity data modeling"
<p>Gravity changes data observed in the network between July 2021 and January 2022 </p> <p>Reference:</p> <p>Insights into the magmatic feeding system of the 2021 eruption at Cumbre Vieja (La Palma, Canary Islands) inferred from gravity data modeling <br> F. G. Montesinos1,7, S. Sainz-Maza2,7, D. Gómez-Ortiz3, J. Arnoso4,7, I. Blanco-Montenegro5,7, M. Benavent1,7 E. Vélez4,7, N. Sánchez6 and T. Martín-Crespo3</p> <p>1 Facultad de CC. Matemáticas, Universidad Complutense de Madrid. Plaza de Ciencias 3, 28040 Madrid, Spain.<br> 2 Observatorio Geofísico Central (IGN). C/ Alfonso XII, 3. 28014 Madrid, Spain.<br> 3 Dpt. Biología y Geología, Física y Química Inorgánica, ESCET, Universidad Rey Juan Carlos. C/Tulipán s/n, 28933 Móstoles, Madrid, Spain.<br> 4 Instituto de Geociencias (IGEO), CSIC-UCM. C/ Doctor Severo Ochoa, 7. 28040 Madrid, Spain.<br> 5 Departamento de Física, Escuela Politécnica Superior, Universidad de Burgos. Avda. de Cantabria s/n, 09006 Burgos, Spain.<br> 6 Instituto Geológico y Minero de España (IGME, CSIC), Unidad Territorial de Canarias, Alonso Alvarado, 43, 2A, 35003 Las Palmas de Gran Canaria, Spain.<br> 7 Research Group ‘Geodesia’, Universidad Complutense de Madrid, Spain.</p> <p><br> Corresponding author: Fuensanta G. Montesinos (fuensant@ucm.es)</p> <p>This research is supported by the project PID2019-104726GB-I00/AEI/10.13039/501100011033 funded by the Spanish Research Agency. Further, the University Complutense of Madrid (grants Financiación Grupos 2021, UCM 2022-GRFN14/22) and the Spanish Ministry of Science and Innovation (RD 1078/2021, funding for research activities of the CSIC-PIE project CSIC-LAPALMA-07) supported this research.</p>
Variability of Eddy Kinetic Energy in the Eurasian Basin of the Arctic Ocean inferred from a Model Simulation at 1-km Resolution (data)
<p>Data for "Variability of Eddy Kinetic Energy in the Eurasian Basin of the Arctic Ocean inferred from a Model Simulation at 1-km Resolution"</p>
Combining formal methods and Bayesian approach for inferring discrete-state stochastic models from steady-state data
<p>Model, data, and a script to a paper of respective name</p>
Repositiry for the article: "Gene regulatory network inference using mixed-norms regularized multivariate model with covariance selection" by Alain Mbebi & Zoran Nikoloski
<p>This is the repository for the manuscript "Gene regulatory network inference using mixed-norms regularized multivariate model with covariance selection" by Alain J. Mbebi & Zoran Nikoloski.</p> <p><strong>Organisation</strong></p> <ol> <li>The folder Codes contains the following R scripts with the K-folds cross-validation option to learn the hyperparameters:</li> </ol> <ul> <li>Mixed_L1L21_GRN.R which computes L1L21-solution</li> <li>Mixed_L1L21G_GRN.R which computes L1L21G-solution</li> <li>Mixed_L2L21_GRN.R which computes L2L21-solution</li> <li>Mixed_L2L21G_GRN.R which computes L2L21G-solution</li> <li>L1L21_Dream5_Scerevisiae_example_run.R is an example run using the L1L21-solution with S. cerevisiae data (Network 4 in DREAM5 challenge) All files needed to successfully run "L1L21_Dream5_Scerevisiae_example_run" are locaded in the folder Codes.</li> </ul> <p>2. The folder Figures contains all figures in the manuscript.</p> <p>3. The folder Inferred-networks contains all network objects for each dataset and each inference methods in the comparative analysis.</p> <p><strong>Dependencies and required packages</strong></p> <p>The following packages are required for the contending approaches in the comparative analysis: "devtools", "foreach", "plyr", "glmnet" and "randomForest".</p> <p><strong>GENIE3</strong></p> <p>The GENIE3 package can be installed from: <a href="http://bioconductor.org/packages/release/bioc/html/GENIE3.html">http://bioconductor.org/packages/release/bioc/html/GENIE3.html</a></p> <p><strong>TIGRESS</strong></p> <p>The TIGRESS repository can be obtained from: <a href="https://github.com/jpvert/tigress">https://github.com/jpvert/tigress</a></p> <p><strong>ENNET</strong></p> <p>The ENNET repository can be obtained from: <a href="https://github.com/slawekj/ennet">https://github.com/slawekj/ennet</a></p> <p><strong>PLSNET</strong></p> <p>The Matlab source code of PLSNET can be obtained from: <a href="https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-016-1398-6#Sec17">https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-016-1398-6#Sec17</a></p> <p><strong>PORTIA</strong></p> <p>The PORTIA repository can be obtained from: <a href="https://github.com/AntoinePassemiers/PORTIA">https://github.com/AntoinePassemiers/PORTIA</a></p> <p><strong>D3GRN</strong></p> <p>The Matlab source code of D3GRN can be obtained from: <a href="https://github.com/chenxofhit/D3GRN">https://github.com/chenxofhit/D3GRN</a></p> <p><strong>Fused-LASSO</strong></p> <p>The fused-LASSO repository can be obtained from: <a href="https://github.com/omranian/inference-of-GRN-using-Fused-LASSO">https://github.com/omranian/inference-of-GRN-using-Fused-LASSO</a></p> <p><strong>ANOVerence</strong></p> <p>Because of some technical issues (e.g code's accessibility: <a href="http://www2.bio.ifi.lmu.de/%CB%9Ckueffner/anova.tar.gz">http://www2.bio.ifi.lmu.de/˜kueffner/anova.tar.gz</a>), we were not able to reproduce ANOVerence results and used the inferred network from DREAM5 challenge instead.</p> <p>4. Although the codes here were tested on Fedora 29 (Workstation Edition) using R (version 4.2.2), they can run under any Linux or Windows OS distributions, as long as all the required packages are compatible with the desired R version.</p>
A Transformer-based Function Symbol Name Inference Model from an Assembly Language for Binary Reversing
<p>This is a dataset and pre-trained model for the official implementation of <a href="https://github.com/agwaBom/AsmDepictor"><strong>AsmDepictor</strong></a>, "A Transformer-based Function Symbol Name Inference Model from an Assembly Language for Binary Reversing", In the 18th ACM Asia Conference on Computer and Communications Security <a href="https://asiaccs2023.org/">AsiaCCS '2023</a></p> <p> </p>
zigzag: A Hierarchical Bayesian Mixture Model for Inferring the Expression State of Genes in Transcriptomes
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Data from: Validating dispersal distances inferred from autoregressive occupancy models with genetic parentage assignments
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Data from: Sharing detection heterogeneity information among species in community models of occupancy and abundance can strengthen inference
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Data from: Integrating niche and occupancy models to infer the distribution of an endemic fossorial snake (Atractus lasallei)
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Including a spatial predictive process in band recovery models improves inference for Lincoln estimates of animal abundance
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Inferring predator-prey interactions from camera traps: A Bayesian co-abundance modelling approach
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Modelling trait heterogeneity and inferring causal links in the macroevolution of growth habit in eudicot angiosperms
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Data from: Assessing the impact of character evolution models on phylogenetic and macroevolutionary inferences from fossil data
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Integrated animal movement and spatial capture-recapture models: simulation, implementation, and inference
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A greenhouse experiment partially supports inferences of ecogeographic isolation from niche models of Clarkia sister species
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Supplementary files from: Improving inference and avoiding over-interpretation of hidden-state diversification models: Specialized plant breeding has no effect on diversification in frogs
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Impact of model assumptions on the inference of the evolution of ectomycorrhizal symbiosis in fungi
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Bayesian inference of ancestral host-parasite interactions under a phylogenetic model of host repertoire evolution
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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.