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109
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ShareScore release 0.9.0
Dataset results
109 results for “prediction accuracy”
Molecular simulations to investigate the impact of N6-methylation in RNA recognition: Improving accuracy and precision of binding free energy prediction
<p>Dataset relative to Molecular dynamics simulation performed for the work "Molecular simulations to investigate the impact of N6-methylation in RNA recognition: Improving accuracy and precision of binding free energy prediction".<br><br>The dataset contains data of 42 alchemical simulations and is subdivided in 4 zip files.<br><br>Folders are named following the scheme: system_configuration_forcefield.<br>Zip file C1 contains .mdp files used for all the simulations.<br><br>Folders corresponding to simulations performed with the fit5_AC ff contains:<br>- topology files (topol.top, topol_RNA_chain_A.itp, topol_RNA_chain_B.itp)<br>- index files needed to reconstruct the demuxed trajectories (replica_index.xvg , replica_index.xvg)<br>- 16 folders, one for each replica (lam0 ... lam15), containing:<br> - final configuration (confout.gro)<br> - log file (md.log)</p> <p> - input file for md run (md.tpr)<br> - energies for the concatenated trajectories recomputed for the realtive replica hamiltonian (ener_trj_conc.edr)<br><br>Folders corresponding to simlations performed with fit_A parametrization only contains .edr files corresponding to energies for the concatenated trajectory computed for 14 set of DeQs drawn from a gaussian distribution, with the relative topologies.<br><br>Supplementary materials relative to simlations performed with fit_A parametrizationcan be found in: https://zenodo.org/records/6498021</p>
Chemperium database for: Geometric Deep Learning for Molecular Property Predictions with Chemical Accuracy Across Chemical Space
<p>The dataset and trained models for the submitted manuscript "Geometric Deep Learning for Molecular Property Prediction with Chemical Accuracy Across Chemical Space"</p> <p>The trained models can be used in combination with the predict module in github.com/mrodobbe/chemperium. More information in README.md.</p> <p><em>When using these datasets, refer directly to the manuscript: https://doi.org/10.1186/s13321-024-00895-0 </em></p>
Incorporation of soil-derived covariates in progeny testing and line selection to enhance genomic prediction accuracy in soybean breeding
<p>The availability of high-dimensional molecular markers has allowed plant breeding programs to maximize their efficiency through the genomic prediction of a phenotype of interest. Yield is a highly complex and quantitative trait whose expression is sensitive to environmental stimuli. In this research, we investigated the potential of incorporating soil texture and its interaction with molecular markers through covariance structures to enhance predictive ability. A total of 797 advanced soybean breeding lines derived from 367 unique bi-parental populations were genotyped using the Illumina Infinium BARCSoySNP6K BeadChip and tested for yield for five years in Tiptonville silt loam, Sharkey clay, and Malden fine sand environments. Four statistical models were considered, including a default GBLUP model (M1), a reaction norm model (M2) accounting for the interaction between molecular markers and the environment (GE), an expansion of M2 including soil type (S), and the interaction between soil type and molecular markers (GS) (M3), and an alternative version of M3 without the GE term. Four cross-validation scenarios simulating progeny testing and line selection were implemented (CV2, CV1, CV0, and CV00). Across environments, the addition of GS in M3 decreased the amount of variability captured by both the environment (-30.4%) and residual (-39.2%) terms as compared to M1. Within environments, the GS term in M3 reduced the variability captured by the residual term by roughly 60% and 30% when compared to M1 and M2, respectively. M3 outperformed all models in CV2 (0.577), CV1 (0.480), and CV0 (0.488). The addition of soil texture seems to structure the environment term revealing its components that could enhance or hinder the predictability of a model. The availability of soil texture before the growing season may maximize the functionality of covariance structures, particularly in scenarios with untested genotypes in untested environments. Genomic selection can optimize the efficiency of a soybean breeding program by allowing the reconsideration of field experimental design, allocation of resources, reduction of preliminary trials, and shortening of the breeding cycle.</p>
Diagnostic Accuracy of Foot Length in Predicting Preterm and Low Birth Weight Using Ultrasound Dating as The Gold Standard in a Rural District of Pakistan
ClinicalTrials.gov study NCT05515211. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
Diagnostic Accuracy of Placental Thickness in Lower Uterine Segment Measured By Ultrasound in Prediction of Placenta Accreta Spectrum in Patients With Placenta Previa. A Diagnostic Test Accuracy Study
ClinicalTrials.gov study NCT05500404. IPD Sharing: NO. Countries: 1. Publications: 7.
Diagnostic Accuracy of the Central Venous Pressure (CVP) Variation to Predict Fluid Responsiveness in Spontaneously Breathing Patients
ClinicalTrials.gov study NCT03780660. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Prediction Accuracy for Langeal Mask Unique TM Size in Pediatric Patient
ClinicalTrials.gov study NCT04215302. IPD Sharing: NO. Countries: 1. Publications: 7.
A Study to Evaluate the Accuracy of a Subset of the Length-109 Probe Set Panel (a Genetic Test) in Predicting Response to Golimumab in Participants With Moderately to Severely Active Ulcerative Coliti
ClinicalTrials.gov study NCT01988961. IPD Sharing: Not stated. Countries: 12. Publications: 2.
Accuracy of Automatic Contusion Volume Scanning for Predicting the Incidence of Pneumonia in Patients With Thoracic Trauma
ClinicalTrials.gov study NCT06750822. IPD Sharing: YES. Countries: 1. Publications: 1.
Accuracy for Predicting Deep Submucosal Invasion
ClinicalTrials.gov study NCT03748667. IPD Sharing: NO. Countries: 3. Publications: 4.
The Accuracy of Computer Software Prediction of Soft Tissue Profile for Patients Undergoing Fixed Orthodontic Treatment
ClinicalTrials.gov study NCT05978856. IPD Sharing: Not stated. Countries: 1. Publications: 11.
Accuracy of Risk Prediction Scores in Pregnant Women
ClinicalTrials.gov study NCT04221048. IPD Sharing: UNDECIDED. Countries: 1. Publications: 9.
Predictive Accuracy of MATRx Plus in Identifying Favorable Candidates for Oral Appliance Therapy
ClinicalTrials.gov study NCT03217383. IPD Sharing: NO. Countries: 1. Publications: 2.
Diagnostic Accuracy of Serum Bilirubin in the Prediction of Perforated Appendicitis
ClinicalTrials.gov study NCT01698099. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Evaluation of the Accuracy, Safety and Robustness of a Single-input-single-output (SISO) Model-based Predictive Closed-loop System to Guide Patient-individualized ICU Sedation
ClinicalTrials.gov study NCT00735631. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Diagnostic Accuracy of M3 in Predicting Colorectal Advanced Adenoma Recurrence (M3-AA)
ClinicalTrials.gov study NCT05144152. IPD Sharing: NO. Countries: 1. Publications: 12.
Accuracy of EEG Slow Wave Activity in Predicting Favourable Outcome in Patients With Hypoxic Brain Injury - A Substudy of STEPCARE Trial
ClinicalTrials.gov study NCT06564675. IPD Sharing: UNDECIDED. Countries: 2. Publications: 1.
Accuracy of CVC Depth Prediction in Internal Jugular Veins: The Difference Between The Andropoulos and ECG Methods
ClinicalTrials.gov study NCT04215250. IPD Sharing: NO. Countries: 1. Publications: 8.
Accuracy and Predictive Values for Colorectal Cancer of Quantitative FIT in Symptomatic Patients in Primary Care
ClinicalTrials.gov study NCT05156307. IPD Sharing: NO. Countries: 1. Publications: 1.
Diagnostic Accuracy of NICE Classification to Predict Deep Submucosal Invasion
ClinicalTrials.gov study NCT02328066. IPD Sharing: Not stated. Countries: 1. Publications: 2.
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.