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23 results for “Logistical regression”
Annual time series of global VIIRS nighttime lights for 2000-2024 at 500-m spatial resolution extrapolated using logistic regression
<p>The <a href="https://eogdata.mines.edu/products/vnl/"><strong>Annual Visible Night Light (VNL) V2</strong></a> (VIIRS) images at 500-m spatial resolution for the period 2012 to 2024 (Elvidge et al., 2021) have been used to extrapolate the values backwards for years 2000–2011. This was done by fitting a logistic regression (per pixel) and then predicting the values for the previous years (see nightlights_stack_500m.R). After consistent time-series have been produced, I also derived the difference between year 2024 and year 2000 (nightlights.difference_viirs.v21_m_500m_s_2000_2024_go_epsg4326_v20230318.tif): this shows average rate of change for the 25 years period. Use with caution: extrapolation of values can lead to artifacts. For most of the land surface, however, it appears that the growth of night lights follows exponential growth function and hence nights in the past can be represented accurately by fitting decay / logistic regression function.</p> <p>Original values from the Annual VNL V2 product have been converted from 0–200 to 0–2000 scale and are available as Cloud-Optimized GeoTIFFs.</p> <p>Principal components (PC1, PC2, PC3, PC4) were derived using SAGA GIS (sums-of-squares-and-cross-products matrix) method. The first PC1 usually matches the long-term mean value, PC2 matches the 1st derivation in values. File "nightlights_dmsp.v10_m_1km_s_19920101_20241231_go_epsg4326_v20251006.tif" contains 33 years 1992 to 2024, but at 1 km resolution.</p> <p>To cite the Annual VNL V2, please use:</p> <ul> <li>Elvidge, C. D., Zhizhin, M., Ghosh, T., Hsu, F. C., & Taneja, J. (2021). <a href="https://doi.org/10.3390/rs13050922">Annual time series of global VIIRS nighttime lights derived from monthly averages: 2012 to 2019</a>. Remote Sensing, 13(5), 922. https://doi.org/10.3390/rs13050922</li> </ul> <p>Historic night light images (1 km resolution) are also available from <a href="https://doi.org/10.6084/m9.figshare.9828827.v10">Figshare</a>:</p> <ul> <li>Li, X., Zhou, Y., Zhao, M., & Zhao, X. (2020). <a href="https://doi.org/10.1038/s41597-020-0510-y">A harmonized global nighttime light dataset 1992–2018</a>. Scientific data, 7(1), 168. https://doi.org/10.1038/s41597-020-0510-y</li> </ul>
Dataset: The effects of class balance on the training energy consumption of logistic regression models
<p>Two synthetic datasets for binary classification, generated with the Random Radial Basis Function generator from WEKA. They are the same shape and size (104.952 instances, 185 attributes), but the "balanced" dataset has 52,13% of its instances belonging to class c0, while the "unbalanced" one only has 4,04% of its instances belonging to class c0. Therefore, this set of datasets is primarily meant to study how class balance influences the behaviour of a machine learning model.</p>
Supporting data for: A method of sexing the human os coxae based on logistic regressions and Bruzek's nonmetric traits
<p>The three following datasets are related to the article <em>A method of sexing the human os coxae based on logistic regressions and Bruzek's nonmetric traits</em> (Santos, Guyomarc'h, Rmoutilova, & Bruzek, 2019):</p> <ul> <li><strong>data_refPELVIS_Santos2019AJPA.csv</strong>: is described as the "reference sample" of 592 ossa coxae in the article. This is the learning dataset available in the <a href="https://gitlab.com/f.santos/pelvis">PELVIS R package</a></li> <li><strong>data_518RightBones_Santos2019AJPA.csv</strong>: the dataset of 518 right ossa coxae used to discuss asymmetry and the impact of lateralization on the sex estimates produced by PELVIS</li> <li><strong>data_3D_Santos2019AJPA.csv</strong>: the virtual coxal data acquired through 99 CT-scan images</li> </ul>
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.
Analysis of vegetation distribution in interior Alaska and sensitivity to climate change using a logistic regression approach
All data are used in the following manuscript which is in prep Analysis of vegetation distribution in interior Alaska and sensitivity to climate change using a logistic regression approach Calef et al.
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 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>
LORIS: a logistic regression-based immunotherapy-response score
<p>This is a repository of input data and code for reproducing the paper titled "LORIS robustly predicts patient outcomes with immune checkpoint blockade therapy using common clinical, pathologic, and genomic features" by Chang et al. (Nature Cancer 2024).</p> <p>Briefly, in this work, Chang et al. developed a new clinical score called the LOgistic Regression-based Immunotherapy-response Score (LORIS) using a transparent and concise 6-feature logistic regression model. LORIS outperforms previous signatures in ICB response prediction and can identify responsive patients, even those with low tumor mutational burden or tumor PD-L1 expression. Importantly, LORIS consistently predicts both objective responses and short-term and long-term survival across multiple cancer types. Moreover, LORIS showcases a near-monotonic relationship with ICB response probability and patient survival, enabling more precise patient stratification across the board. As the method is accurate, interpretable, and only utilizes a few readily measurable features, it could help improve clinical decision-making practices in precision medicine to maximize patient benefit.</p>
Data from: Improving performance of hurdle models using rare-event weighted logistic regression: An application to maternal mortality data
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Exploring Factors Promoting Dependency Updates with Survival Time Analysis and Logistic Regression
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Intravoxel incoherent motion model of diffusion weighted imaging and diffusion kurtosis imaging in differentiating of local colorectal cancer recurrence from scar/fibrosis tissue by multivariate logistic regression analysis
<p>We uploaded mean of diffusion coefficient (MD) and mean of diffusional Kurtosis values of 56 patients related to the manuscript: Fusco, Roberta, Vincenza Granata, Mario Sansone, Robert Grimm, Paolo Delrio, Daniela Rega, Fabiana Tatangelo, Antonio Avallone, Nicola Raiano, Giuseppe Totaro, Vincenzo Cerciello, Biagio Pecori, and Antonella Petrillo. 2020. "Intravoxel Incoherent Motion Model of Diffusion Weighted Imaging and Diffusion Kurtosis Imaging in Differentiating of Local Colorectal Cancer Recurrence from Scar/Fibrosis Tissue by Multivariate Logistic Regression Analysis" Applied Sciences 10, no. 23: 8609. https://doi.org/10.3390/app10238609</p>
Development and Validation of a Logistic Regression Algorithm to Predict the Risk of Obstetric Anal Sphincter Injury.
ClinicalTrials.gov study NCT05218837. IPD Sharing: NO. Countries: 1. Publications: 4.
Logistic Regression and Elastic Net Regularization for the Diagnosis of Fibromyalgia
ClinicalTrials.gov study NCT04088747. IPD Sharing: NO. Countries: 1. Publications: 12.
Predicting Postoperative Pulmonary Infection in Elderly Patients Undergoing Major Surgery: a Study Based on Logistic Regression and Machine Learning Models
ClinicalTrials.gov study NCT06491459. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data from: Logistic regression analysis of factors influencing the effectiveness of intensive sound masking therapy in patients with tinnitus
Objectives: To investigate factors influencing the effectiveness intensive sound masking therapy on tinnitus using Logistic Regression Analysis. Design: The study used a retrospective cross-section analysis. Participants: 102 patients with tinnitus were recruited at the Sun Yat-sen Memorial Hospital of Sun Yat-sen University, China. Intervention: Intensive sound masking therapy was used as an intervention approach for patients with tinnitus. Primary and secondary outcome measures: participants underwent audiological investigations and tinnitus pitch and loudness matching measurements, followed by intensive sound masking therapy. The Tinnitus Handicap Inventory (THI) was used as the outcome measure pre- and post-treatment. Multivariate logistic regression was performed to investigate the association of demographic and audiological factors with effective therapy. Results: According to the THI score changes pre-and post-sound masking intervention, fifty-one participants were categorised into an effective group, the remaining 51 participants were placed in a non-effective group. Those in the effective group were significantly younger than those in the non-effective group (p=0.012). Significantly more participants had flat audiogram configurations in the effective group (p=0.04). Multivariable logistic regression analysis showed that age (OR=0.96, 95% CI: 0.93, 0.99, p=0.007), audiometric configuration (p=0.027) and THI score pre-treatment (OR=1.04, 95% CI: 1.02, 1.07, p<0.001) were significantly associated with therapeutic effectiveness. Further analysis showed that patients with flat audiometric configurations were 5.45 times more likely to respond to intervention than those with high-frequency steeply sloping audiograms (OR=5.45, 95% CI: 1.67, 17.86, p=0.005). Conclusion: Audiometric configuration, age and THI scores appear to be predictive for the effectiveness of sound masking treatment. Gender, tinnitus characteristics and hearing threshold measures seem not to be related to treatment effectiveness. Further randomized control study is needed to provide further evidence of the effectiveness of prognostic factors in tinnitus interventions.
Repurposing Domperidone in Secondary Progressive MS - A Simon 2-Stage Phase 2 Futility Trial - Table e1: Results of the binary logistic regression model
<p><b>Objective:</b> To assess whether treatment with the generic drug domperidone can reduce the progression of disability in secondary progressive multiple sclerosis (SPMS), we conducted a phase 2 futility trial following the Simon two-stage design.</p> <p><b>Methods:</b> We enrolled patients in an open-label, Simon two-stage, single-center, phase 2, single-arm futility trial at the Calgary MS Clinic if they met the following criteria: age 18 to 60 years, SPMS, screening EDSS score of 4.0 to 6.5 and screening T25FW of 9 seconds or more. Patients received domperidone 10mg QID for one year. The primary outcome was worsening of disability, defined as worsening of the T25FW performance by 20% or more at 12 months compared to at baseline. This trial is registered with ClinicalTrials.gov, number NCT02308137.</p> <p><b>Results:</b> Between February 13<sup>th</sup>, 2015 and January 3<sup>rd</sup>, 2020, 110 patients were screened, 81 received treatment, 64 completed follow-up, of whom 62 were analysed. The study did not meet its primary endpoint: 22 of 62 (35%) patients experienced significant worsening of disability, which is close to the expected proportion of 40%, and above the pre-defined futility threshold. Patients with higher prolactin levels during the study had a significantly lower risk of disability progression, which may warrant further investigation. Domperidone treatment was reasonably well tolerated, but adverse events occurred in 84% and serious adverse events in 15% of patients.</p> <p><b>Conclusions:</b> Domperidone treatment could not reject futility in reducing disability progression in SPMS. The Simon two-stage trial model may be a useful model for phase 2 studies in progressive MS.</p> <p><b>Classification of Evidence:</b> This study provides Class III evidence that in individuals with secondary progressive multiple sclerosis participating in a futility trial, domperidone treatment could not reject futility in reducing disability progression at 12 months.</p>
Association between beliefs about mediations and adherence to medications: a stepwise binary logistic regression model
<p>Dataset of research article.</p>
Repurposing Domperidone in Secondary Progressive MS - A Simon 2-Stage Phase 2 Futility Trial - Table e1: Results of the binary logistic regression model
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Data from: Logistic regression analysis of factors influencing the effectiveness of intensive sound masking therapy in patients with tinnitus
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Data from: Three-level mixed-effects logistic regression analysis reveals complex epidemiology of swine rotaviruses in diagnostic samples from North America
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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.