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103 results for “regression modeling”
Data from: Compressor map regression modelling based on partial least squares
In this work, two kinds of partial least squares modelling methods are applied to predict a compressor map: one uses a Power function polynomial as the basis function (PLSO), and the other uses a trigonometric function polynomial (PLSN). To demonstrate the potential capabilities of PLSO and PLSN for a typical interpolated prediction and extrapolated prediction, they are compared with two other classical data-driven modelling methods, namely, the look-up table and artificial neural network. PLSO and PLSN are also compared to each other. The results show that PLSO and PLSN have a better prediction performance than the look-up table and the artificial neural network, especially for the extrapolated prediction. At the same time, the computational time is also decreased sharply. Compared with PLSO, PLSN is characterized with higher prediction accuracy and shorter computational time than PLSO. It can be expected that PLSN can be time-saving and improve the accuracy of a thermodynamic model of a diesel engine.
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>
Data from: Incorporating single-step strategy into random regression model to enhance genomic prediction of longitudinal trait
In prediction of genomic values, single-step method has been demonstrated to outperform multi-step methods. In statistical analyses of longitudinal traits, random regression test-day model (RR-TDM) has clear advantages over other models. Our goal in this study was to evaluate the performance of the model integrating both single-step and RR-TDM prediction methods, called single-step random regression test-day model (SS RR-TDM), in comparison with the pedigree-based RR-TDM and genomic best linear unbiased prediction (GBLUP) model. We performed extensive simulations to exploit potential advantages of SS RR-TDM over the other two models under various scenarios with different level of heritability, the number of QTL as well as the selection scheme. SS RR-TDM was found to achieve the highest accuracy and unbiasedness under all scenarios, exhibiting robust prediction ability in longitudinal trait analyses. Moreover, SS RR-TDM showed better persistency of accuracy over generations than GBLUP model. In addition, we also found that the SS RR-TDM had advantages over RR-TDM and GBLUP in terms of a real dataset of human contributed by the GAW18 workshop. The findings in our study firstly proved the feasibility and advantages of the SS RR-TDM, and further enhanced strategies for the genomic prediction of longitudinal traits in the future.
Global oceanic PON concentration products derived from Aqua-MODIS through a Gaussian Process Regression model
<p><span>This dataset integrates the monthly PON concentration products from 2002 to 2022 of the global ocean, which were derived from Aqua-MODIS based on our newly developed Gaussian Process Regression models.</span></p>
Data from: Multiple regression modelling for estimating endocranial volume in extinct Mammalia
The profound evolutionary success of mammals has been linked to behavioral and life-history traits, many of which have been tied to brain size. However, studies of the evolution of this key trait have yet to explore the full potential of the fossil record, being limited by the difficulty of obtaining endocranial data from fossils. Using measurements of endocranial volume, length, height, and width of the braincase in 503 adult specimens from 199 extant species, representing 99 of 133 extant mammalian families, we expand upon a simple method of using multiple regression to develop a formula for estimating brain size from external skull measurements. We also examined non- mammalian synapsids to assess the phylogenetic limits of our model's application. Model-predicted volume correlates strongly with measured volume (R2 = 0.993) and prediction error is between 16% and 19%. Error decreases if models developed for well-sampled subclades such as primates or rodents are used, demonstrating that some differential evolution of the relationship between brain size and skull size has occurred. However, reanalysis using phylogenetically independent contrasts demonstrates weak phylogenetic dependency, indicating that our model is appropriate for estimating the endocranial volume of species of unknown phylogenetic affinity. Thus, the model represents a generally applicable, fast and cost-efficient way to dramatically expand the taxonomic and temporal scope of mammalian brain size data sets. Even endocranial volumes of taxa with highly derived crania, such as cetaceans and monotremes, can be estimated confidently. However, the model works best for generalized placental crania. Fundamental differences in cranial architecture suggest that the model cannot provide accurate estimates of endocranial volume in non-mammalian synapsids more basal than Morganucodon (ca. 200 Ma). Therefore, use of the model for taxa phylogenetically distant from the mammalian crown group is not warranted, but it might be used to establish relative brain sizes between closely related subgroups.
Association between beliefs about mediations and adherence to medications: a stepwise binary logistic regression model
<p>Dataset of research article.</p>
Data from: Identifying drivers of parallel evolution: a regression model approach
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Data from: Multiple regression modelling for estimating endocranial volume in extinct Mammalia
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Data from: Compressor map regression modelling based on partial least squares
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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: Mammalian metabolic allometry: do intraspecific variation, phylogeny, and regression models matter?
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Data from: Incorporating single-step strategy into random regression model to enhance genomic prediction of longitudinal trait
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Data from: Improving estimates of environmental change using multilevel regression models of Ellenberg indicator values
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A novel irreversible TEAD inhibitor, SWTX-143, blocks the Hippo pathway and causes tumor regression in preclinical mesothelioma models [SubQ]
GEO Series GSE222962. Homo sapiens. 24 samples. Type: Expression profiling by high throughput sequencing.
Stability selection for regression-based models of transcription factor-DNA binding specificity
GEO Series GSE47026. Homo sapiens. 4 samples. Type: Genome binding/occupancy profiling by array.
A novel irreversible TEAD inhibitor, SWTX-143, blocks the Hippo pathway and causes tumor regression in preclinical mesothelioma models [Invitro]
GEO Series GSE222960. Homo sapiens. 12 samples. Type: Expression profiling by high throughput sequencing.
Loss of MeCP2 in the rat models regression, impaired sociability and transcriptional deficits of Rett syndrome
GEO Series GSE83323. Rattus norvegicus. 8 samples. Type: Expression profiling by high throughput sequencing.
MicroRNA expression profiling of livers from the conditional doxycyline-regulatable c-Myc-driven liver tumor mouse model during tumor development and regression.
GEO Series GSE152920. Mus musculus; Murid betaherpesvirus 1; Murid gammaherpesvirus 4. 16 samples. Type: Non-coding RNA profiling by array.
Single cell analysis of atherosclerosis baseline and regression macrophages-surgical model
GEO Series GSE97941. Mus musculus. 244 samples. Type: Expression profiling by high throughput sequencing.
Symbolic Regression Model To Predict Choledocholithiasis
ClinicalTrials.gov study NCT04410848. IPD Sharing: NO. Countries: 1. Publications: 0.
ScienceDex guides
Understand access before you commit
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.