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1,773
datasets available to search
ShareScore release 0.9.0
Dataset results
1,773 results for “Predictive model”
Network Modeling of Liver Metabolism to Predict Plasma Metabolite Changes During Short-Term Fasting in the Laboratory Rat: Liver Transcriptome Changes in Study 3
GEO Series GSE123987. Rattus norvegicus. 16 samples. Type: Expression profiling by high throughput sequencing.
Modeling the next-generation of rhabdomyosarcoma organoids to predict effective drug combinations [methylation]
GEO Series GSE248182. Homo sapiens. 25 samples. Type: Methylation profiling by array.
Human midbrain dopaminergic neuronal differentiation markers predict cell therapy outcome in a Parkinson's disease model
GEO Series GSE204795. Homo sapiens. 12 samples. Type: Expression profiling by high throughput sequencing.
Network Modeling of Liver Metabolism to Predict Plasma Metabolite Changes During Short-Term Fasting in the Laboratory Rat: Liver Transcriptome Changes in Study 2
GEO Series GSE124004. Rattus norvegicus. 16 samples. Type: Expression profiling by high throughput sequencing.
Developing a gene expression model for predicting ventilator-associated pneumonia in trauma patients: a pilot study.
GEO Series GSE30385. Homo sapiens. 20 samples. Type: Expression profiling by array.
Predicting drug response in human prostate cancer from preclinical analysis of in vivo mouse models
GEO Series GSE69214. Mus musculus. 35 samples. Type: Expression profiling by array.
Improving Computational Efficiency of Prediction in Model-based Prognostics Using the Unscented Transform
Model-based prognostics captures system knowledge in the form of physics-based models of components, and how they fail, in order to obtain accurate predictions of end of life (EOL). EOL is predicted based on the esti- mated current state distribution of a component and ex- pected profiles of future usage. In general, this requires simulations of the component using the underlying mod- els. In this paper, we develop a simulation-based pre- diction methodology that achieves computational effi- ciency by performing only the minimal number of sim- ulations needed in order to accurately approximate the mean and variance of the complete EOL distribution. This is performed through the use of the unscented trans- form, which predicts the means and covariances of a distribution passed through a nonlinear transformation. In this case, the EOL simulation acts as that nonlinear transformation. In this paper, we review the unscented transform, and describe how this concept is applied to efficient EOL prediction. As a case study, we develop a physics-based model of a solenoid valve, and perform simulation experiments to demonstrate improved com- putational efficiency without sacrificing prediction accu- racy.
An Efficient Deterministic Approach to Model-based Prediction Uncertainty
Prognostics deals with the prediction of the end of life (EOL) of a system. EOL is a random variable, due to the presence of process noise and uncertainty in the future inputs to the sys- tem. Prognostics algorithms must account for this inherent uncertainty. In addition, these algorithms never know exactly the state of the system at the desired time of prediction, or the exact model describing the future evolution of the system, accumulating additional uncertainty into the predicted EOL. Prediction algorithms that do not account for these sources of uncertainty are misrepresenting the EOL and can lead to poor decisions based on their results. In this paper, we explore the impact of uncertainty in the prediction problem. We develop a general model-based prediction algorithm that incorporates these sources of uncertainty, and propose a novel approach to efficiently handle uncertainty in the future input trajecto- ries of a system by using the unscented transform. Using this approach, we are not only able to reduce the computa- tional load but also estimate the bounds of uncertainty in a deterministic manner, which can be useful to consider during decision-making. Using a lithium-ion battery as a case study, we perform several simulation-based experiments to explore these issues, and validate the overall approach using experi- mental data from a battery testbed.
Phenotype-driven precision oncology in patient-derived tumor models predict therapeutic response in squamous cell carcinoma
GEO Series GSE84323. Homo sapiens. 152 samples. Type: Expression profiling by high throughput sequencing.
Human In-vitro Inflammatory Liver Model Recapitulates Immune-Associated Drug Efects with High Predictivity
GEO Series GSE189320. Homo sapiens. 35 samples. Type: Expression profiling by high throughput sequencing.
A long context RNA foundation model for predicting transcriptome architecture
GEO Series GSE280041. Homo sapiens. 104 samples. Type: Expression profiling by high throughput sequencing.
Deep Learning Models for Cell Cycle Phase Prediction from Single-Cell RNA Sequencing Data
GEO Series GSE293316. Homo sapiens. 10 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
Predicting Oncology Drug-Induced Cardiotoxicity with Donor-Specific iPSC-CMs – Model Verification with Doxorubicin
GEO Series GSE242692. Homo sapiens. 88 samples. Type: Expression profiling by high throughput sequencing.
Applications of zebrafish embryo models to predict developmental toxicity for agrochemical product development
GEO Series GSE254415. Danio rerio. 224 samples. Type: Expression profiling by high throughput sequencing.
A machine learning breast cancer prediction model based on a panel from circulating exosomal miRNAs
GEO Series GSE197020. Homo sapiens. 96 samples. Type: Non-coding RNA profiling by high throughput sequencing.
Predicting HMX bioavailability using microarray gene expression data and regression modeling
GEO Series GSE42866. Eisenia fetida. 120 samples. Type: Expression profiling by array.
Machine Learning Prediction Models for Mitral Valve Repairability and Mitral Regurgitation Recurrence in Patients Undergoing Surgical Mitral Valve Repair
<p>This record contains raw data related to the article "Machine Learning Prediction Models for Mitral Valve Repairability and Mitral Regurgitation Recurrence in Patients Undergoing Surgical Mitral Valve Repair"</p> <p>Abstract: Background: Mitral valve regurgitation (MR) is the most common valvular heart disease and current variables associated with MR recurrence are still controversial. We aim to develop a machine learning-based prognostic model to predict causes of mitral valve (MV) repair failure and MR recurrence. Methods: 1000 patients who underwent MV repair at our institution between 2008 and 2018 were enrolled. Patients were followed longitudinally for up to three years. Clinical and echocardiographic data were included in the analysis. Endpoints were MV repair surgical failure with consequent MV replacement or moderate/severe MR (>2+) recurrence at one-month and moderate/severe MR recurrence after three years. Results: 817 patients (DS1) had an echocardiographic examination at one-month while 295 (DS2) also had one at three years. Data were randomly divided into training (DS1: n = 654; DS2: n = 206) and validation (DS1: n = 164; DS2 n = 89) cohorts. For intra-operative or early MV repair failure assessment, the best area under the curve (AUC) was 0.75 and the complexity of mitral valve prolapse was the main predictor. In predicting moderate/severe recurrent MR at three years, the best AUC was 0.92 and residual MR at six months was the most<br> important predictor. Conclusions: Machine learning algorithms may improve prognosis after MV repair procedure, thus improving indications for correct candidate selection for MV surgical repair.</p>
Figure 70 in Predicted and recorded distribution maps of the genus Phanaeus (Coleoptera: Scarabaeidae). Supplementary material of Distribution, Regionalization, and Diversity of the dung beetle genus Phanaeus MacLeay (Coleoptera: Scarabaeidae) using Species Distribution Models
Figure 70: Predicted distribution of Phanaeus triangularis species group.
Figure 63 in Predicted and recorded distribution maps of the genus Phanaeus (Coleoptera: Scarabaeidae). Supplementary material of Distribution, Regionalization, and Diversity of the dung beetle genus Phanaeus MacLeay (Coleoptera: Scarabaeidae) using Species Distribution Models
Figure 63: Predicted and recorded distribution of Phanaeus quadridens.
Figure 62 in Predicted and recorded distribution maps of the genus Phanaeus (Coleoptera: Scarabaeidae). Supplementary material of Distribution, Regionalization, and Diversity of the dung beetle genus Phanaeus MacLeay (Coleoptera: Scarabaeidae) using Species Distribution Models
Figure 62: Predicted and recorded distribution of Phanaeus palliatus.
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.