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ShareScore release 0.9.0
Dataset results
209 results for “probabilistic”
Validation of an Online Knee Pain Map and Questionnaire: A Probabilistic Diagnostic Tool
ClinicalTrials.gov study NCT01492244. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Predictors of Probabilistic Selection Task Performance
ClinicalTrials.gov study NCT04798105. IPD Sharing: NO. Countries: 1. Publications: 0.
A Study to Evaluate a Multiple Model Probabilistic Predictive Controller (MMPPC) for Closed Loop Insulin Delivery
ClinicalTrials.gov study NCT01492062. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Interest in Probabilistic Antibiotic Therapy With Broad-spectrum Beta-lactams in Orthopedic Surgery
ClinicalTrials.gov study NCT06191601. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Budget Impact Analysis of Discontinuing Tyrosin Kinase Inhibitors in Patients With Chronic Myeloid Leukemia Achieving a Complete Molecular Response by Using Probabilistic Markov Approach
ClinicalTrials.gov study NCT03540654. IPD Sharing: NO. Countries: 1. Publications: 0.
Deep Brain Stimulation for Parkinson's Disease: Probabilistic STN Targeting Under General Anaesthesia Without Micro-electrode Recordings vs Current Targeting Procedure
ClinicalTrials.gov study NCT04884412. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Creation of a Probabilistic Atlas of Normal Cerebral Uptake of 18F-FDG in PET/MRI.
ClinicalTrials.gov study NCT05355857. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Probabilistic Targeting of the VIM
ClinicalTrials.gov study NCT03696420. IPD Sharing: NO. Countries: 1. Publications: 0.
Prospective Evaluation of Probabilistic Predictions of Epileptic Seizure Risk Using the EPIDAY Tool
ClinicalTrials.gov study NCT07068919. IPD Sharing: NO. Countries: 1. Publications: 0.
A probabilistic generative model for quantification of DNA modifications enables analysis of demethylation pathways [T-cells]
GEO Series GSE68575. Mus musculus. 18 samples. Type: Methylation profiling by high throughput sequencing.
VIPR: A probabilistic algorithm for analysis of microbial detection microarrays
GEO Series GSE21407. dengue virus type 4; La Crosse virus; Rift Valley fever virus; Toscana virus; Kyasanur Forest disease virus; Orthohantavirus seoulense; dengue virus type 1; dengue virus type 3; Ebola virus - Gabon (1994-1997); Sudan ebolavirus; Ngari virus; Mammarenavirus amapariense; Mammarenavirus mopeiaense; Mammarenavirus tacaribeense; Orthohantavirus hantanense; Orthonairovirus haemorrhagiae; Flaviviridae; Arenaviridae; unidentified; Peribunyaviridae; Mammarenavirus juninense; Mammarenavirus choriomeningitidis; Mammarenavirus guanaritoense; Mammarenavirus lassaense; Mammarenavirus machupoense; Mammarenavirus praomyidis; Orthohantavirus puumalaense; dengue virus type 2; Yellow fever virus; Filoviridae; Omsk hemorrhagic fever virus; California encephalitis virus; Rocio virus; Zaire ebolavirus; Reston ebolavirus; Lake Victoria marburgvirus - Angola2005; Mammarenavirus brazilense; Mammarenavirus ippyense. 110 samples. Type: Other.
Continuous-trait probabilistic model for comparing multi-species functional genomic data
GEO Series GSE111733. Nomascus leucogenys; Pan troglodytes; Pongo pygmaeus; Chlorocebus aethiops; Homo sapiens. 5 samples. Type: Other.
Probabilistic modeling of personalized drug combinations from integrated chemical screen and molecular data in sarcoma
GEO Series GSE128766. Homo sapiens; Mus musculus. 3 samples. Type: Expression profiling by high throughput sequencing.
Dataset for Probabilistic Optimization for High-Level Synthesis
<p>High-level synthesis (HLS) tools automatically transform a high-level program, for example in C/C++ into a low-level hardware description. A key challenge in HLS tools is scheduling, i.e. determining the start time of all the operations in the untimed program. There are three approaches to scheduling: static, dynamic and hybrid. A major shortcoming of existing approaches to scheduling is that the tools either assume the worst-case timing behaviour, which can cause significant performance loss or area overhead, or use simulation-based approaches, where can cause significant time to explore a large number of program traces. </p> <p>To rectify this, we propose a probabilistic model to efficiently explore timing behaviour of HLS hardware from all these scheduling approaches. We show how to model the hardware behaviour into Petri nets for performance analysis. Our framework offers insights to assist hardware engineers and HLS tools in setting their expectations of the hardware performance from the input code and performing optimal hardware optimisation. </p> <p>As a particular application, we show how to automatically infer the optimal initiation interval~(II) for statically-scheduled components forming part of a dynamically-scheduled circuit. An empirical evaluation on a range of benchmarks suggests that by using this approach, on average we achieve 9x speedup for estimating an optimal II with 2% overhead in area-delay product (ADP) compared to the design with an II by exhaustive search, while the static analysis in Vitis HLS causes 112% ADP overhead and the throughput analysis in Dynamatic causes 17% ADP overhead.</p>
Data for: A complete search of combinatorial peptide library greatly benefited from probabilistic incorporation of prior knowledge
<p>The dataset contains source data for the analyses performed in the article "A complete search of combinatorial peptide library greatly benefited from probabilistic incorporation of prior knowledge". For a detailed description, read the document "Supplementary-material.pdf" in the "Metadata" folder.</p>
Facilitating Exceptional Execution Auditing via Probabilistic Reasoning
Open the record for dataset details and reuse information.
Probabilistic Risk Modeling for Disorder Post Traumatic Stress (PTSD) After the Attacks in Paris In November 2015
ClinicalTrials.gov study NCT02853513. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.
Probabilistic Model-Based Diagnosis for Electrical Power Systems
We present in this article a case study of the probabilistic approach to model-based diagnosis. Here, the diagnosed system is a real-world electrical power system, namely the Advanced Diagnostic and Prognostic Testbed (ADAPT) located at the NASA Ames Research Center. Our probabilistic approach is formally well-founded, and based on Bayesian networks and arithmetic circuits. We pay special attention to meeting two of the main challenges model development and real-time reasoning often associated with real-world application of model-based diagnosis technologies. To address the challenge of model development, we develop a systematic approach to representing electrical power systems as Bayesian networks, supported by an easy-touse specication language. To address the real-time reasoning challenge, we compile Bayesian networks into arithmetic circuits. Arithmetic circuit evaluation supports real-time diagnosis by being predictable and fast. In experiments with the ADAPT Bayesian network, which contains 503 discrete nodes and 579 edges and produces accurate results, the time taken to compute the most probable explanation using arithmetic circuits has a mean of 0.2625 milliseconds and a standard deviation of 0.2028 milliseconds. In comparative experiments, we found that while the variable elimination and join tree propagation algorithms also perform very well in the ADAPT setting, arithmetic circuit evaluation was an order of magnitude or more faster. **Reference:** O. J. Mengshoel, M. Chavira, K. Cascio, S. Poll, A. Darwiche, and S. Uckun. "Probabilistic Model-Based Diagnosis: An Electrical Power System Case Study”. Accepted to IEEE Transactions on Systems, Man, and Cybernetics, Part A, 2009.
Methods for Probabilistic Fault Diagnosis: An EPS Case Study
Health management systems that more accurately and quickly diagnose faults that may occur in different technical systems on-board a vehicle will play a key role in the success of future NASA missions. We discuss in this paper the diagnosis of abrupt continuous (or parametric) faults within the context of probabilistic graphical models, more specifically Bayesian networks that are compiled to arithmetic circuits. This paper extends our previous research, within the same probabilistic setting, on diagnosis of abrupt discrete faults. Our approach and diagnostic algorithm ProDiagnose are domain-independent; however we use an electrical power system testbed called ADAPT as a case study. In one set of ADAPT experiments, performed as part of the 2009 Diagnostic Challenge, our system turned out to have the best performance among all competitors. In a second set of experiments, we show how we have recently further significantly improved the performance of the probabilistic model of ADAPT. While these experiments are obtained for an electrical power system testbed, we believe they can easily be transitioned to real-world systems, thus promising to increase the success of future NASA missions. **Reference:** B. W. Ricks and O. J. Mengshoel, "Methods for Probabilistic Fault Diagnosis: An Electrical Power System Case Study." In Proc. of the First Annual Conference of the Prognostics and Health Management Society (PHM-09), San Diego, CA, September 27 – October 1, 2009. **BibTex Reference:** @inproceedings{ricks09methods, author = {Ricks, B. W. and Mengshoel, O. J.}, title = {Methods for Probabilistic Fault Diagnosis: An Electrical Power System Case Study}, booktitle = {Proc. of the Annual Conference of the Prognostics and Health Management Society (PHM-09)}, address = {San Diego, CA}, month = sep, year = {2009} }
Entropy-based probabilistic fatigue damage prognosis and algorithmic performance comparison
In this paper, a maximum entropy-based general framework for probabilistic fatigue damage prognosis is investigated. The proposed methodology is based on an underlying physics-based crack growth model. V arious uncertainties from measurements, modeling, and parameter estimations are considered to describe the stochastic process of fatigue damage accumulation. A probabilistic prognosis updating procedure based on the maximum relative entropy concept is proposed to incorporate measurement data. Markov Chain Monte Carlo (MCMC) technique is used to provide the posterior samples for model updating in the maximum entropy approach. Experimental data are used to demonstrate the operation of the proposed probabilistic prognosis methodology. A set of prognostics-based metrics are employed to quantitatively evaluate the prognosis performance and compare the proposed method with the classical Bayesian updating algorithm. In particular, model accuracy, precision and convergence are rigorously evaluated in* addition to the qualitative visual comparison.
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.