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384 results for “risk model”
Figure 3 from: Desvignes V, Buschhardt T, Guillier L, Sanaa M (2019) Quantitative microbial risk assessment for Salmonella in eggs. Food Modelling Journal 1: e39643. https://doi.org/10.3897/fmj.1.39643
Figure 3 Probability of illness according to storage duration.
Figure 1 from: Desvignes V, Buschhardt T, Guillier L, Sanaa M (2019) Quantitative microbial risk assessment for Salmonella in eggs. Food Modelling Journal 1: e39643. https://doi.org/10.3897/fmj.1.39643
Figure 1 Global method of risk assessment.
Data from: A methylation-to-expression feature model for generating accurate prognostic risk scores and identifying disease targets in clear cell kidney cancer
Many researchers now have available multiple high-dimensional molecular and clinical datasets when studying a disease. As we enter this multi-omic era of data analysis, new approaches that combine different levels of data (e.g. at the genomic and epigenomic levels) are required to fully capitalize on this opportunity. In this work, we outline a new approach to multi-omic data integration, which combines molecular and clinical predictors as part of a single analysis to create a prognostic risk score for clear cell renal cell carcinoma. The approach integrates data in multiple ways and yet creates models that are relatively straightforward to interpret and with a high level of performance. Furthermore, the proposed process of data integration itself captures relationships in the data that represent highly disease-relevant functions.
Figure 7 from: Basak S, Christy J, Guillier L, Audiat-Perrin F, Sanaa M, Tenenhaus-Aziza F, Bect J, Vazquez E (2024) Quantitative risk assessment of Haemolytic and Uremic Syndrome (HUS) from consumption of raw milk soft cheese. Food and Ecological Systems Modelling Journal 5: e109502. https://doi.org/10.3897/fmj.5.109502
Figure 7 Output module.
Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk (sequence model release)
<p>(This is the updated version that has been converted a standard pytorch model format)</p> <p>This is the deep learning sequence model used in </p> <p>Jian Zhou, Chandra L. Theesfeld, Kevin Yao, Kathleen M. Chen, Aaron K. Wong, and Olga G. Troyanskaya, Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk, Nature Genetics, 2018.</p> <p>Note the full software is available from https://github.com/FunctionLab/ExPecto and this release is created for the convenience of use and under the same non-commercial license. The model weights can be loaded with pytorch load_state_dict function (for an example please find <a href="https://github.com/FunctionLab/ExPecto/blob/master/chromatin.py">https://github.com/FunctionLab/ExPecto/blob/master/chromatin.py</a>). We also provide a web server for browsing mutations with strong predicted effects at https://hb.flatironinstitute.org/expecto/, which are currently limited to mutations within 1kb to TSS or are 1000 Genomes variants.</p> <p>Trivia: we code-named our models with whale names. This model has an unofficial codename DeepSEA "Beluga".</p>
Microbiota: Its Role in Chronic Inflammation, IDB and Risk of Colorectal Cancer. Evaluation of a Predictive Prognostic Model With Therapeutic Implications
ClinicalTrials.gov study NCT07106463. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Risk Factors and Deep Learning Model for CI-AKI
ClinicalTrials.gov study NCT06596785. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Model Study on Cervical Cancer Screening Strategies and Risk Prediction
ClinicalTrials.gov study NCT06204133. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Incidence, Risk Factors, and Risk Model of Acute Kidney Injury in Pediatric Patients Who Undergoing Surgery for Congenital Heart Disease
ClinicalTrials.gov study NCT02081235. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Analysis of Risk Factors and Establishment of Early Warning Model for Pulmonary Complications
ClinicalTrials.gov study NCT04967651. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Construction and Validation of Risk Prediction Model for PICC Catheter Displacement
ClinicalTrials.gov study NCT04924959. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Risk Factors, Prognosis and Prediction Models for Placenta Accreta Without Prior Cesarean Section
ClinicalTrials.gov study NCT06383923. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Minimizing Risk and Maximizing Outcomes in Geriatric Patients Through Integrated Clinical Pharmacy Services in an Innovative Model of Community Practice
ClinicalTrials.gov study NCT01351441. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Development and Validation of a Risk Prediction Model for Ischemic Stroke in Acute Myocardial Infarction Without Comorbid Atrial Fibrillation
ClinicalTrials.gov study NCT07171892. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Prediction Model for the Risk of Developing Foot Ulcers in Diabetes
ClinicalTrials.gov study NCT07307183. IPD Sharing: Not stated. Countries: 1. Publications: 0.
A.I and Machine Learning Based Risk Prediction Model to Improve the Clinical Management of Endometrial Cancer.
ClinicalTrials.gov study NCT06841653. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Risk of Recurrent Venous Thrombosis: A Validation Study of the Vienna Prediction Model
ClinicalTrials.gov study NCT01972243. IPD Sharing: Not stated. Countries: 1. Publications: 0.
A Biobehavioral Model of Diabetes Risk in Chinese Immigrants
ClinicalTrials.gov study NCT02449213. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Development and Validation of a Clinical Risk Prediction Model for Early Acute Kidney Injury Within 48 Hours After Liver Transplantation
ClinicalTrials.gov study NCT06750770. IPD Sharing: NO. Countries: 1. Publications: 0.
The Effect of a Community-based LAT-treated Management Model on the Violence Risk of Patients With Schizophrenia
ClinicalTrials.gov study NCT03434210. IPD Sharing: Not stated. 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.