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103 results for “regression modeling”
Regression models generated by APRANK (computational prioritization of antigenic proteins and peptides from complete pathogen proteomes)
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Vertebrate-habitat relationships: Logistic regression models predict probability of occurrence of bird and small mammal species in western Oregon
Logistic regression models predicting probability of occurrence of bird and of small-mammal species were produced using animal-habitat data sets from throughout western Oregon (Garman and Cole 1999 - Vertebrate Habitat Relationships Data Bank (VHRDB), Report to Coastal Landscape Analysis and Modeling Study). Regression coefficients, variables, and metrics related to model predictions are provided here under Entity 1, and in VHRDB as VERTLOGR.
Conjunto de dados Modelo de Regressão Aplicado à Previsão de Preços SPOT de Energia Elétrica (Dataset Regression Model Applied to Electric Energy SPOT Price Forecasting)
<p>Esse conjunto de dados utilizou DataSets de duas fontes distintas: CCEE e ONS. Como são órgãos públicos os dados são acurados, transparentes, confiáveis e de boa qualidade. Os dados de entrada possuem as variáveis que são utilizadas no modelo atual do PLD, já citado. São elas: as datas, o armazenamento de água, a ENA, a expectativa de ENA para a próxima semana e a carga.</p> <p>As datas são dados diários entre janeiro de 2013 e janeiro de 2017. O armazenamento de água é dado por submercado e apresentado em porcentagem da capacidade máxima. A ENA e a expectativa dela para a semana seguinte são apresentadas em porcentagem a partir das chuvas realizadas convertidas em MWmédio pelas previsões feitas utilizando dados históricos (1932-2007). A carga está em MWmédio. E o PLD em R$/MWh.</p> <p>Os soma dos dados dos quatro submercados (SE/CO, SU, NE, NO) de cada dado nos fornece a informação do Sistema Nacional Interligado (SIN).</p> <p>As variáveis de carga, armazenamento e ENA foram retiradas do histórico de operações do site da ONS, disponibilizados para download em ‘csv’. E o PLD do site da CCEE, disponibilizados em ‘xls’.</p> <p>Foram mesclados a partir das datas formando o arquivo de entrada para o modelo utilizado nos experimento</p> <p> </p> <p>Metadados / Metadata</p> <p>Storage of water: percentage of storage of water by submarket.<br> ENA and expectative of ENA: presented by percentage of previsions of rains that happen converted on MWmedium by previsions did with a historic (1932-2007). Data are by submarket.<br> Charge: charge by submarket give on MWmedium.<br> PLD: give on R$/MWh<br> The sum of each variable is the data of the total system.</p> <p> </p> <p> </p>
31 ChEMBL data sets for regression modeling
<p>From ChEMBL version 17, 31 compound data sets have been selected for regression modeling. Compounds had to be active against human targets in a direct inhibition/binding assay with highest ChEMBL confidence score and Ki values below 100 micromolar. Multiple Ki values for the same compound were averaged if they fell into the same order of magnitude, or else they were disregarded. Duplicates, known pan-assay interference, and other reactive molecules were removed. Only sets with at least 500 compounds were considered.</p> <p> </p> <p>Note: The SD files contain a field "pKi"; note however that this field contains the Ki value in nM units, not the logarithmic value.</p>
Compositional Characterization of Glassy Volcanic Material From VNIR and MIR Spectra Using Partial Least Squares Regression Models
<p>This is supporting data for the paper titled "Compositional Characterization of Glassy Volcanic Material From VNIR and MIR Spectra Using Partial Least Squares Regression Models" by Leight et al. (submitted to JGR-P 11/23). Table S1 lists each spectrum used to train PLS models, its source, and which training datasets the spectrum was included in. Zip files contain the MIR and VNIR PLS model files. Model files are .asc, and can be run using the code at Ytsma, (2022), https://doi.org/10.5281/zenodo.7347345. </p>
Data for: A new threshold selection method for species distribution models with presence-only data: extracting the mutation point of the P/E curve by threshold regression
<p>Selecting thresholds to convert continuous predictions of species distribution models proves critical for many real-world applications and model assessments. Prevalent threshold selection methods for presence-only data require unproven pseudo-absence data or subjective researchers' decisions. This study proposes a new method, Boyce-Threshold Quantile Regression (BTQR), to determine thresholds objectively without pseudo-absence data. We summarize that the mutation point is a typical shape feature of the predicted-to-expected (P/E) curve after reviewing relevant articles. Analysis based on source-sink theory suggests that this mutation point may represent a transition in habitat types and serve as an appropriate threshold. Threshold regression is introduced to accurately locate the mutation point.</p> <p>To validate the effectiveness of BTQR, we used four virtual species of varying prevalence and a real species with reliable distribution data. Six different species distribution models were employed to generate continuous suitability predictions. BTQR and nine other traditional methods transformed these continuous outputs into binary results. Comparative experiments show that BTQR has advantages in terms of accuracy, applicability, and consistency over the existing methods.</p>
Meta-regression Models Describing the Effects of Essential Oils and Added Lactic Acid Bacteria on Staphylococcus aureus Inactivation in Cheese
<p>Recording of the talk “Meta-regression models describing the effects of essential oils and added lactic acid bacteria on <em>Staphylococcus aureus</em> inactivation in cheese”, presented by Beatriz Nunes Silva at the 2020 International Association for Food Protection Annual Meeting, IAFP, Online virtual meeting (26-28 Oct 2020).</p>
A Clustering Approach to Improve IntraVoxel Incoherent Motion Maps from DW-MRI using Conditional Auto-Regressive Bayesian Model
<p>Simulated data generated and used in the paper "A Clustering Approach to Improve IntraVoxel Incoherent Motion Maps from DW-MRI using Conditional Auto-Regressive Bayesian Model" are here available.</p> <p>Results generated from both simulated and clinical datasets are also available on the excel tables.</p>
Deep learning generates custom-made logistic regression models for explaining how breast cancer subtypes are classified
<p>Breast cancer is the most frequently found cancer in women and the one most often subjected to genetic analysis. Nonetheless, it has been causing the largest number of women's cancer-related deaths. PAM50, the intrinsic subtype assay for breast cancer, is beneficial for diagnosis and stratified treatment but does not explain each subtype's mechanism. Nowadays, deep learning can predict the subtypes from genetic information more accurately than conventional statistical methods. However, the previous studies did not directly use deep learning to examine which genes associate with the subtypes. Ours is the first study on a deep-learning approach to reveal the mechanisms embedded in the PAM50-classified subtypes. We developed an explainable deep learning model called a point-wise linear model, which uses a meta-learning approach to generate a custom-made logistic regression model for each sample. Logistic regression is familiar to physicians and medical informatics researchers, and we can use it to analyze which genes are important for subtype prediction. The custom-made logistic regression models generated by the point-wise linear model for each subtype used the specific genes selected in other subtypes compared to the conventional logistic regression model: the overlap ratio is less than twenty percent. And analyzing the point-wise linear model's inner state, we found that the point-wise linear model used genes relevant to the cell cycle-related pathways. The results of this study suggest the potential of our explainable deep learning to play a vital role in cancer treatment.</p>
data sets from "Updated trends of the stratospheric ozone vertical distribution in the 60S–60N latitude range based on the LOTUS regression model"
<p>Monthly means data sets from satellite, ground-based and model records used in the article entitled: "Updated trends of the stratospheric ozone vertical distribution in the 60 S–60 N latitude range based on the LOTUS regression model"</p> <p> </p>
data sets from "Updated trends of the stratospheric ozone vertical distribution in the 60S–60N latitude range based on the LOTUS regression model"
<p>Monthly means data sets from satellite, ground-based and model records used in the article entitled: "Updated trends of the stratospheric ozone vertical distribution in the 60 S–60 N latitude range based on the LOTUS regression model".</p> <p>Information about and the most recent versions of each dataset can be found at their individual source locations:</p> <p>Merged satellite datasets</p> <ol> <li>SBUV MOD – https://acd-ext.gsfc.nasa.gov/Data_services/merged/index.html (NASA GSFC, USA)</li> <li>SBUV COH: https://ftp.cpc.ncep.noaa.gov/SBUV_CDR/ (NOAA, USA).</li> <li>GOZCARDS: https://www.earthdata.nasa.gov/esds/competitive-programs/measures/gozcards (JPL, NASA, USA)</li> <li>SWOOSH: https://csl.noaa.gov/groups/csl8/swoosh/ (NOAA, USA).</li> <li>SAGE-CCI-OMPS and MEGRIDOP datasets are available through https://climate.esa.int/en/projects/ozone/data/ and ftp://cci_web@ftp-ae.oma.be/esacci (ESA Climate Office). They are provided by FMI, Finland</li> <li>SAGE-SCIAMACHY-OMPS: data record is available upon registration via the following link: http://www.iup.uni-bremen.de/DataRequest/ (U. Bremen, Germany).</li> <li>SAGE-OSIRIS-OMPS: downloading instructions can be found at https://research-groups.usask.ca/osiris/data-products.php#OSIRISLevel3andMergedDataProducts (U. Saskatchewan, Canada).</li> </ol> <p>Ground-based records:</p> <ol> <li>Umkehr – https://gml.noaa.gov/aftp/data/ozwv/Dobson/AC4/Umkehr/Monthly/ (NOAA, USA)</li> <li>ozonesondes – https://hegiftom.meteo.be/datasets/ozonesondes (HEGIFTOM). Measurements at the various stations are provided by the following institutions: <ul> <li>Hohenpeissenberg: DWD, Germany</li> <li>Payerne:MeteoSwiss, Switzerland</li> <li>OHP, CNRS, France</li> <li>Hilo, NOAA, USA</li> <li>Lauder, NIWA, New Zealand</li> </ul> </li> <li>lidar: <a href="http://www.ndacc.org/">http://www.ndacc.org/</a> . Measurement at the various stations are provided by the following institutions: <ul> <li>Hohenpeissenberg: DWD, Germany</li> <li>OHP: CNRS, France</li> <li>MLO: JPL, NASA, USA</li> <li>Lauder: NIWA, New Zealand</li> </ul> </li> <li>FTIR spectrometers – <a href="http://www.ndacc.org/">http://www.ndacc.org/</a> Three sites only provided quality checked measurements relevant for the article. For other ozone FTIR measurements, data in <a href="http://www.ndacc.org/">http://www.ndacc.org/</a> must be used. Measurement used in the article are provided by the following institutions: <ul> <li>Zugspitze: KIT, Germany</li> <li>Jungfraujoch: ULiège, GIRPAS team, Belgium</li> <li>Lauder: NIWA, New Zealand</li> </ul> </li> <li>Microwave spectrometers: <a href="http://www.ndacc.org/">http://www.ndacc.org/</a> Measurement at the various stations are provided by the following institutions: <ul> <li>Payerne: MeteoSwiss, Switzerland</li> <li>Mauna Loa: NRL, USA</li> <li>Lauder: NRL, USA</li> </ul> </li> </ol> <p>Chemistry Climate Model (CCM) CCMI simulations are avilable at https://blogs.reading.ac.uk/ccmi</p>
Cloud computing is one of the most popular and sophisticated technologies adopted by organizations worldwide. Some world-leading organizations enhance their efficiency and effectiveness by using cloud computing technology. Working from home (WFH) has been a popular trend among organizations during the coronavirus (COVID-19) pandemic. The COVID-19 saw a breakthrough in work cultures and environments where working from home was a remarkable success in remote working environments, despite being a rare phenomenon in Sri Lanka. Yet, it is argued that the deployment of work from home has not been effective among Sri Lankan business organizations due to a lack of IT infrastructure, facilities, and knowledge. The purpose of the study is to investigate the impact of cloud computing, embracing the service models (Infrastructure as a Service, Platform as a Service, and Software as a Service) as theoretical lenses and testing the COVID-19 as the moderator. The study has been conducted based on a deductive approach and adopted a stratified random sampling method. The sample consisted of 384 IT employees among those who had experienced working from home. The study utilized multiple regression and found that cloud computing service models significantly impact work from home with the moderating effect of COVID-19.
<p>Cloud computing is one of the most popular and sophisticated technologies adopted by organizations worldwide. Some world-leading organizations enhance their efficiency and effectiveness by using cloud computing technology. Working from home (WFH) has been a popular trend among organizations during the coronavirus (COVID-19) pandemic. The COVID-19 saw a breakthrough in work cultures and environments where working from home was a remarkable success in remote working environments, despite being a rare phenomenon in Sri Lanka. Yet, it is argued that the deployment of work from home has not been effective among Sri Lankan business organizations due to a lack of IT infrastructure, facilities, and knowledge. The purpose of the study is to investigate the impact of cloud computing, embracing the service models (Infrastructure as a Service, Platform as a Service, and Software as a Service) as theoretical lenses and testing the COVID-19 as the moderator. The study has been conducted based on a deductive approach and adopted a stratified random sampling method. The sample consisted of 384 IT employees among those who had experienced working from home. The study utilized multiple regression and found that cloud computing service models significantly impact work from home with the moderating effect of COVID-19.</p>
Data from: Improving performance of hurdle models using rare-event weighted logistic regression: An application to maternal mortality data
<p>In this paper, the performance of hurdle models in rare events data is improved by modifying their binary component. The rare-event weighted logistic regression model is adopted in place of logistic regression to deal with class imbalance due to rare events. Poisson Hurdle Rare Event Weighted Logistic Regression (REWLR) and Negative Binomial Hurdle (NBH) REWLR are developed as two-part models which use the REWLR model to estimate the probability of a positive count and a Poisson or NB zero-truncated count model to estimate non-zero counts. The obtained results are numerically validated and then discussed from both the mathematical and the maternal mortality perspective. Numerical simulations are also presented to give a more complete representation of the model dynamics. Results obtained suggest that NB Hurdle REWLR is the best-performing model for zero-inflated count data due to rare events.</p>
Supplementary Materials include results of simulation experiments to investigate the impact of phylogenetic regression with model violations.
<p>Modern comparative biology owes much to phylogenetic regression. At its conception, this technique sparked a revolution that armed biologists with phylogenetic comparative methods (PCMs) for disentangling evolutionary correlations from those arising from hierarchical phylogenetic relationships. Over the past few decades, the phylogenetic regression framework has become a paradigm of modern comparative biology that has been widely embraced as a remedy for shared ancestry. However, recent evidence has sown doubt over the efficacy of phylogenetic regression, and PCMs more generally, with the suggestion that many of these methods fail to provide an adequate defense against unreplicated evolution—the primary justification for using them in the first place. Importantly, some of the most compelling examples of biological innovation in nature result from abrupt lineage-specific evolutionary shifts, which current regression models are largely ill-equipped to deal with. Here we explore a solution to this problem by applying robust linear regression to comparative trait data. We formally introduce robust phylogenetic regression to the PCM toolkit with linear estimators that are less sensitive to model violations than the standard least-squares estimator, while still retaining high power to detect true trait associations. Our analyses also highlight an ingenuity of the original algorithm for phylogenetic regression based on independent contrasts, whereby robust estimators are particularly effective. Collectively, we find that robust estimators hold promise for improving tests of trait associations and offer a path forward in scenarios where classical approaches may fail. Our study joins recent arguments for increased vigilance against unreplicated evolution and a better understanding of evolutionary model performance in challenging–yet biologically important–settings.</p>
Data for publication of "Gaussian process regression-based Bayesian optimisation (G-BO) of model parameters - a WRF model case study of southeast Australia heat extremes"
<p>Implementation of Gaussian process regression-based Bayesian optimisation (G-BO) using the emcee package (<a href="https://emcee.readthedocs.io/en/stable/" rel="nofollow">https://emcee.readthedocs.io/en/stable/</a>).</p> <p>For more information about the implementation of G-BO in optimising the Weather Research and Forecasting (WRF) model parameters, please refer to the paper - <a href="https://essopenarchive.org/doi/full/10.22541/essoar.171292045.52489731" rel="nofollow">Gaussian process regression-based Bayesian optimisation (G-BO) of model parameters - a WRF model case study of southeast Australia heat extremes</a>.</p> <p><code>G-BO_script.ipynb</code> implements the GPR-based Bayesian optimisation using the Affine Invariant Markov chain Monte Carlo (MCMC) Ensemble sampler.</p> <ul> <li><strong>QMC_sobol_samples</strong>: This file contains the 128 parameter samples across the parameter space of three sensitive parameters utilizing the Quasi Monte-Carlo (QMC) Sobol sequence design.</li> <li><strong>nmae_all_128_ens_T_Rh</strong>: This file contains the normalised mean absolute error (NMAE) values of temperature (T) and relative humidity (Rh) of the 128 parameter sample WRF simulations. For more details, please refer to <a href="https://essopenarchive.org/doi/full/10.22541/essoar.171292045.52489731" rel="nofollow">this link</a>.</li> </ul>
Evaluation of Predictive Capabilities of Regression Models and Artificial Neural Networks for Density and Viscosity Measurements of Different Biodiesel-Diesel-Vegetable Oil Ternary Blends
<p>In this section, it was given that Annex Figures and Annex Tables related to the article "Evaluation of Predictive Capabilities of Regression Models and Artificial Neural Networks for Density and Viscosity Measurements of Different Biodiesel-Diesel-Vegetable Oil Ternary Blends" published in "Environmental and Climate Technologies" journal. </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>
Unpacking the "black box": improving ecological interpretation of regression based models
<p><strong>Aim</strong><br>Many tree species distribution models use black-box machine learning techniques that often neglect interpretative aspects and instead focus mainly on maximising predictive accuracy. In this study, we outline an interpretative modelling framework to gain better ecological insights while mapping abundance patterns of six North American species.</p> <p><strong>Location</strong><br>Continental United States and Canada</p> <p><strong>Methods</strong><br>We develop an innovative procedure using regression trees by stabilising variance and mapping dominant rules which we term 'optimized regression tree bagging for interpretation and mapping' (ORTBIM). We apply this technique to understand ecological features influencing the abundance patterns of three eastern (<em>Pinus</em> <em>strobus</em>, <em>Acer</em> <em>saccharum</em>, and <em>Quercus</em> <em>montana</em>), and three western (<em>Picea</em> <em>engelmannii</em>, <em>Pinus</em> <em>ponderosa</em>, and <em>Pseudotsuga</em> <em>menziesii</em>) tree species in North America. For these species, we assess and map the dominant climate-terrain interactions that partly determine abundance patterns in the eastern and western regions. In the process, we examine the role of varying responses and scales and explore finer-scale species climate-terrain niches and non-linear relationships.</p> <p><strong>Results</strong><br>Our study emphasizes the prominent role of elevation and heat-moisture variables in the west and the greater importance of seasonal precipitation and seasonal temperature in the east. The abundance patterns under future climate (SSP5–8.5) show climate-terrain habitats shifting northward and westward into Canada and Alaska for the eastern species, and predominantly north-westward for the western species.</p> <p><strong>Conclusion</strong><br>Our interpretative modelling framework can be used to gain a more comprehensive understanding of the abundance patterns across the full species range, to formulate better predictive models, and to facilitate improved management practices under climate change.</p>
A structured evaluation of regression models for predicting CO2 concentration from plasma emission spectra, dataset
<p>Dataset for publication: <a href="https://doi.org/10.1016/j.sab.2022.106467">https://doi.org/10.1016/j.sab.2022.106467</a>.</p> <p>The recorded spectra are stored as comma separated values, the set includes a meta data-file (.mat-file), and a column descriptions (columns.pdf).</p>
Data for: Leveraging spatio-temporal genomic breeding value estimates of dry matter yield and herbage quality in ryegrass via random regression models
<p>Joint modeling of correlated multi-environment and multi-harvest data of perennial crop species may offer advantages in prediction schemes and a better understanding of the underlying dynamics in space and time. The goal of the present study was to investigate the relevance of incorporating the longitudinal dimension of within-season multiple measurements of forage perennial ryegrass traits in a reaction norm model setup that additionally accounts for genotype-environment interactions (G×E). Genetic parameters and accuracy of genomic breeding value (gEBV) predictions were investigated by fitting three random regression models (gRRM) using Legendre polynomial functions to the data. Genomic DNA sequencing of family pools of diploid perennial ryegrass was performed using DNA nanoball-based technology and yielded 56,645 single nucleotide polymorphisms which were used to calculate the allele frequency-based genomic relationship matrix. Biomass yield's estimated additive genetic variance and heritability values were higher in later harvests. The additive genetic correlations were moderate to low in early measurements and peaked at intermediates, with fairly stable values across the environmental gradient, except for the initial harvest data collection. This led to the conclusion that complex (G×E) arises from spatial and temporal dimensions in the early season, with lower re-ranking trends thereafter. In general, modeling the temporal dimension with a second-order orthogonal polynomial improved the accuracy of gEBV prediction for nutritive quality traits, but no gain in prediction accuracy was detected for dry matter yield. This study leverages the flexibility and usefulness of gRRM models for perennial ryegrass breeding and can be readily extended to other multi-harvest crops.</p>
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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.