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17 results for “latent variable”
BioVAE: a pre-trained latent variable language model for biomedical text mining
<p>We release BioVAE, the first large-scale pre-trained latent variable language model for the biomedical domain, which uses the OPTIMUS framework to train on large volumes of biomedical text.</p> <p>This version contains the pre-trained models for text mining tasks such as named entity recognition or relation extraction, and text generation task.</p> <p>Explanation of each file: (lt32: latent_size = 32, beta05: beta=0.5)</p> <ul> <li>pm-full-lt32-beta00</li> <li>pm-full-lt32-beta05</li> <li>pm-full-lt768-beta00</li> <li>pm-full-lt768-beta05</li> <li>pm-full-generation</li> </ul>
Data from: Correcting for cell-type effects in DNA methylation studies: reference-based method outperforms latent variable approaches in empirical studies
Based on an extensive simulation study, McGregor and colleagues recently recommended the use of surrogate variable analysis (SVA) to control for the confounding effects of cell-type heterogeneity in DNA methylation association studies in scenarios where no cell-type proportions are available. As their recommendation was mainly based on simulated data, we sought to replicate findings in two large-scale empirical studies. In our empirical data, SVA did not fully correct for cell-type effects, its performance was somewhat unstable, and it carried a risk of missing true signals caused by removing variation that might be linked to actual disease processes. By contrast, a reference-based correction method performed well and did not show these limitations. A disadvantage of this approach is that if reference methylomes are not (publicly) available, they will need to be generated once for a small set of samples. However, given the notable risk we observed for cell-type confounding, we argue that, to avoid introducing false-positive findings into the literature, it could be well worth making this investment.
Figure 1 in Figure 5 in On the Integrity of Online Testing for Introductory Statistics Courses: A Latent Variable Approach
Figure 1 The kinship amcnS individuals cf three matrilineal units within trccp WW-1. The ciocles oepoesent cemales and squaoes oepoesent males. Matoilineal units: HL, honglian unit; CM, changmao unit; BD, bandian unit.
Figure 3 in On the Integrity of Online Testing for Introductory Statistics Courses: A Latent Variable Approach
Figure 3 Box and whiskeo ploqs oc canonical scooes coo Indo-Pacicic humpback dolphins. The squaoes, boxes and whiskeos oepoesenq means, sqandaod eoooos and sqandaod desiaqions, oespecqiselv, in (A) and medians, 25 and 75 quaoqiles and minima-maxima, oespecqiselv, in (B). Indo-Pacicic humpback dolphins coom qhe Jiulong Riseo Esquaov + Peaol Riseo Esquaov %JRE + PRE) and Taiwanese waqeos.
Figure 1 in On the Integrity of Online Testing for Introductory Statistics Courses: A Latent Variable Approach
Figure 1 Map oc qhe squdv aoea. Sampling locaqions oc Indo-Pacicic humpback dolphins analvsed in qhis squdv. The goeen shaded aoea oepoesenqs qhe known disqoibuqion oc qhese dolphins, and qhe oed sqao and vellow ciocles show qhe locaqions coom wheoe qhe holoqvpe and paoaqvpe specimens oc qhe Taiwanese humpback dolphin Sousa chinensis taiwanensis weoe collecqed, oespecqiselv. Some baqhvmeqoic conqouos in qhe Taiwan Sqoaiq aoe also shown.
Figure 4 in On the Integrity of Online Testing for Introductory Statistics Courses: A Latent Variable Approach
Figure 4 Foequencv disqoibuqions oc canonical scooes oc Indo-Pacicic humpback dolphins. The disqoibuqions coo Indo-Pacicic humpback dolphins coom Taiwanese waqeos and qhe Jiulong Riseo Esquaov + Peaol Riseo Esquaov aoe shown in goeen and vellow, oespecqiselv. The doqqed lines indicaqe qhe locaqions wheoe 75% oc qhe disqoibuqions aoe cound.
Programs and data used for Extracting latent variables from forecast ensembles and advancements in similarity metric utilizing optimal transport
<p>This is compiled from the program and output data using in Nishizawa (2024).</p> <p> </p> <p>Nishizawa, 2024: Extracting latent variables from forecast ensembles and advancements in similarity metric utilizing optimal transport. submitted to JGR: Machine Learning and Computation.</p>
The Kling-Gupta efficiency (KGE) and latent variable data to analyze the dynamic changes and driving mechanisms of predictability
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Supplementary material 1 from: Grace JB, Steiner M (2021) A protocol for modelling generalised biological responses using latent variables in structural equation models. One Ecosystem 6: e67320. https://doi.org/10.3897/oneeco.6.e67320
A protocol for modelling generalised biological responses using latent variables in structural equation models
Supplementary material 3 from: Grace JB, Steiner M (2021) A protocol for modelling generalised biological responses using latent variables in structural equation models. One Ecosystem 6: e67320. https://doi.org/10.3897/oneeco.6.e67320
A protocol for modelling generalised biological responses using latent variables in structural equation models
Supplementary material 2 from: Grace JB, Steiner M (2021) A protocol for modelling generalised biological responses using latent variables in structural equation models. One Ecosystem 6: e67320. https://doi.org/10.3897/oneeco.6.e67320
A protocol for modelling generalised biological responses using latent variables in structural equation models
Data from: Correcting for cell-type effects in DNA methylation studies: reference-based method outperforms latent variable approaches in empirical studies
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Figure 2 in Figure 5 in On the Integrity of Online Testing for Introductory Statistics Courses: A Latent Variable Approach
Figure 2 Ccmbined prcbability cf exclusicn increasinS with number cf lcci.
Personality and chronotype: a latent variable perspective
<p>This dataset serves as supplementary material for the referenced article.</p>
Skabbholmen data from: Concurrent ordination: Simultaneous unconstrained and constrained latent variable modeling
<ol> <li>In community ecology, unconstrained ordination can be used to indirectly explore drivers of community composition, while constrained ordination can be used to directly relate predictors to an ecological community. However, existing constrained ordination methods do not explicitly account for community composition that cannot be explained by the predictors, so that they have the potential to misrepresent community composition if not all predictors are available in the data.</li> <li>We propose and develop a set of new methods for ordination and Joint Species Distribution Modelling (JSDM) as part of the Generalized Linear Latent Variable Model (GLLVM) framework, that incorporate predictors directly into an ordination. This includes a new ordination method that we refer to as concurrent ordination, as it simultaneously constructs unconstrained and constrained latent variables. Both unmeasured residual covariation and predictors are incorporated into the ordination by simultaneously imposing reduced rank structures on the residual covariance matrix and on fixed-effects.</li> <li>We evaluate the method with a simulation study, and show that the proposed developments outperform Canonical Correspondence Analysis (CCA) for Poisson and Bernoulli responses, and perform similar to Redundancy Analysis (RDA) for normally distributed responses, the two most popular methods for constrained ordination in community ecology. Two examples with real data further demonstrate the benefits of concurrent ordination, and the need to account for residual covariation in the analysis of multivariate data.</li> <li>This article contextualizes the role of constrained ordination in the GLLVM and JSDM frameworks, while developing a new ordination method that incorporates the best of unconstrained and constrained ordination, and which overcomes some of the deficiencies of existing classical ordination methods.</li> </ol>
Skabbholmen data from: Concurrent ordination: Simultaneous unconstrained and constrained latent variable modeling
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Figure 2 in On the Integrity of Online Testing for Introductory Statistics Courses: A Latent Variable Approach
Figure 2 Scaqqeoploq oc spoqqing inqensiqv on doosal cins and bodies oc Indo-Pacicic humpback dolphins. Mean scooes oc qhe spoqqing inqensiqies on qhe doosal cins ss. bodies oc Indo-Pacicic humpback dolphins coom qhe easqeon Taiwan Sqoaiq %goeen squaoes), Peaol Riseo Esquaov %vellow squaoes) and qhe Jiulong Riseo Esquaov %blue ciocles). Small svmbols = 1 qo 5 indisiduals; medium svmbols = 6 qo 10 indisiduals; laoge svmbols =>10 indisiduals.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.