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493
datasets available to search
ShareScore release 0.9.0
Dataset results
493 results for “Predictive factors”
Transcriptome profiling of maize transcription factor mutants to probe gene regulatory network predictions
GEO Series GSE280139. Zea mays. 113 samples. Type: Expression profiling by high throughput sequencing.
Mitotic chromosome binding predicts transcription factor properties in interphase
GEO Series GSE119784. Mus musculus. 60 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
QBiC-Pred: Quantitative Predictions of Transcription Factor Binding Changes Due to Sequence Variants
GEO Series GSE130837. Arabidopsis thaliana; synthetic construct; Homo sapiens; Mus musculus. 12 samples. Type: Other.
An Atlas of the Binding Specificities of Transcription Factors in Pseudomonas aeruginosa Directs Prediction of Novel Regulators in Virulence
GEO Series GSE151518. Pseudomonas aeruginosa PAO1. 940 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Other.
Predictive Biophysical Neural Network Modeling of a Compendium of in vivo Transcription Factor DNA Binding Profiles for Escherichia coli
GEO Series GSE268698. Escherichia coli K-12; Escherichia coli str. K-12 substr. MG1655. 424 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Identification of Predictive Factors in Advanced Pancreatic Cancer Through Extracellular Vesicle Analysis
GEO Series GSE213341. Homo sapiens. 21 samples. Type: Non-coding RNA profiling by high throughput sequencing.
SLIDE analysis of the scRNA-seq data using an interpretable factor-analysis machine learning framework, that moves beyond predictive biomarkers to try and infer latent factors underlying LS pathophysi
GEO Series GSE288490. Homo sapiens. 16 samples. Type: Expression profiling by high throughput sequencing.
Gene regulatory network analysis predicts cooperating transcription factor regulons required for FLT3-ITD+ AML growth [RNA-seq]
GEO Series GSE236772. Homo sapiens. 27 samples. Type: Expression profiling by high throughput sequencing.
Gastrointestinal Stromal Tumor Enhancers Support a Transcription Factor Network Predictive of Clinical Outcome
GEO Series GSE95863. Homo sapiens. 20 samples. Type: Expression profiling by high throughput sequencing.
Ceramide metabolism alterations contribute to Tumor Necrosis Factor-induced melanoma dedifferentiation and predict resistance to immune checkpoint inhibitors in advanced melanoma patients [A375]
GEO Series GSE270740. Homo sapiens. 8 samples. Type: Expression profiling by high throughput sequencing.
Similarity Regression predicts evolution of transcription factor sequence specificity
GEO Series GSE121420. synthetic construct. 682 samples. Type: Other.
Membranous Expression of Ectodomain Isoforms of the Epidermal Growth Factor Receptor (EGFR) Predicts Outcome after Chemoradiotherapy of Lymph Node Negative Cervical Cancer
GEO Series GSE29817. Homo sapiens. 151 samples. Type: Expression profiling by array.
Data from: Assimilating MODIS data-derived minimum input data set and water stress factors into CERES-Maize model improves regional corn yield predictions
Crop growth models and remote sensing are useful tools for predicting crop growth and yield, but each tool has inherent drawbacks when predicting crop growth and yield at a regional scale. To improve the accuracy and precision of regional corn yield predictions, a simple approach for assimilating Moderate Resolution Imaging Spectroradiometer (MODIS) products into a crop growth model was developed, and regional yield prediction performance was evaluated in a major corn-producing state, Illinois, USA. Corn growth and yield were simulated for each grid using the Crop Environment Resource Synthesis (CERES)-Maize model with minimum inputs comprising planting date, fertilizer amount, genetic coefficients, soil, and weather data. Planting date was estimated using a phenology model with a leaf area duration (LAD)-logistic function that describes the seasonal evolution of MODIS-derived leaf area index (LAI). Genetic coefficients of the corn cultivar were determined to be the genetic coefficients of the maturity group [included in Decision Support System for Agrotechnology Transfer (DSSAT) 4.6], which shows the minimum difference between the maximum LAI derived from the LAD-logistic function and that simulated by the CERES-Maize model. In addition, the daily water stress factors were estimated from the ratio between daily leaf area/weight growth rates estimated from the LAD-logistic function and that simulated by the CERES-Maize model under the rain-fed and auto-irrigation conditions. The additional assimilation of MODIS data-derived water stress factors and LAI under the auto-irrigation condition showed the highest prediction accuracy and precision for the yearly corn yield prediction (R2 is 0.78 and the root mean square error is 0.75 t ha-1). The present strategy for assimilating MODIS data into a crop growth model using minimum inputs was successful for predicting regional yields, and it should be examined for spatial portability to diverse agro-climatic and agro-technology regions.
raw data - Clinical Characteristics and Risk Factors for Prediction of Severity in Patients with COVID-19
Open the record for dataset details and reuse information.
Quality of life in patients with multiple sclerosis and caregivers. Predictive factors: An observational study
<p>Multiple sclerosis (MS) is a neurodegenerative and autoimmune disease, which can significantly affect not only the quality of life (QoL) of affected people but also that of their careers who care for them. The main objective of this study was to assess the extent to which the patient's clinical, cognitive and psychological conditions affect his or her QoL and that of the caregiver.</p> <p>Methods</p> <p>We examined a number of patients with clinically defined MS. In this study 78 patient-assistant pairs were enrolled.</p> <p>Results</p> <p>Our results showed a significant correlation between the change in the patient's state of health and the quality of life of caregivers, especially in specific social and work areas. In addition, the age and the physical and mental health of patients emerged as predictive factors on the quality of caregivers.</p> <p>Conclusions</p> <p>This study has shown that degenerative and chronic diseases, such as multiple sclerosis, can be predictors of stress and poor quality of life for careers.</p> <p>Future studies should further clarify the impact that the psychological conditions of MS patients have on the quality of life of careers.</p>
Predictive factors for delirium in octogenarian women hospitalized for a hip fracture
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Evaluation of Predictive Factors for Psoas Tendinitis After First-line Total Hip Arthroplasty
ClinicalTrials.gov study NCT05029648. IPD Sharing: NO. Countries: 1. Publications: 0.
Predictive Factors for the Diagnosis of Early Noninvasive Ventilation Equipment
ClinicalTrials.gov study NCT03452618. IPD Sharing: NO. Countries: 1. Publications: 0.
French Cohort Study of Chronic Heart Failure Patients With Central Sleep Apnoea Eligible for Adaptive Servo-Ventilation (PaceWave, AutoSet CS, AirCurve 10CS): Predictive Factors of Poor Compliance (FA
ClinicalTrials.gov study NCT02356367. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Role of Cord Blood Cytokines and Perinatal Factors in Prediction of Retinopathy of Prematurity
ClinicalTrials.gov study NCT04769661. IPD Sharing: YES. Countries: 0. Publications: 0.
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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.