Data integration: Combined imaging and electrophysiology data in the cloud.

There has been an increasing effort to correlate electrophysiology data with imaging in patients with refractory epilepsy over recent years. IEEG.org ...
2MB Sizes 2 Downloads 10 Views

Recommend Documents


Bioinformatics and Microarray Data Analysis on the Cloud.
High-throughput platforms such as microarray, mass spectrometry, and next-generation sequencing are producing an increasing volume of omics data that needs large data storage and computing power. Cloud computing offers massive scalable computing and

Data integration to prioritize drugs using genomics and curated data.
Genomic alterations affecting drug target proteins occur in several tumor types and are prime candidates for patient-specific tailored treatments. Increasingly, patients likely to benefit from targeted cancer therapy are selected based on molecular a

A survey on personal data cloud.
Personal data represent the e-history of a person and are of great significance to the person, but they are essentially produced and governed by various distributed services and there lacks a global and centralized view. In recent years, researchers

GIFT-Cloud: A data sharing and collaboration platform for medical imaging research.
Clinical imaging data are essential for developing research software for computer-aided diagnosis, treatment planning and image-guided surgery, yet existing systems are poorly suited for data sharing between healthcare and academia: research systems

Provenance based data integrity checking and verification in cloud environments.
Cloud computing is a recent tendency in IT that moves computing and data away from desktop and hand-held devices into large scale processing hubs and data centers respectively. It has been proposed as an effective solution for data outsourcing and on

NeuroElectro: a window to the world's neuron electrophysiology data.
The behavior of neural circuits is determined largely by the electrophysiological properties of the neurons they contain. Understanding the relationships of these properties requires the ability to first identify and catalog each property. However, i

Predicting Alzheimer's Disease Using Combined Imaging-Whole Genome SNP Data.
The growing public threat of Alzheimer's disease (AD) has raised the urgency to discover and validate prognostic biomarkers in order to predicting time to onset of AD. It is anticipated that both whole genome single nucleotide polymorphism (SNP) data

Data integration with high dimensionality.
We consider situations where the data consist of a number of responses for each individual, which may include a mix of discrete and continuous variables. The data also include a class of predictors, where the same predictor may have different physica

Data Integration for Heterogenous Datasets.
More and more, the needs of data analysts are requiring the use of data outside the control of their own organizations. The increasing amount of data available on the Web, the new technologies for linking data across datasets, and the increasing need