Multidimensional data integration and relationship inference

2002 ◽  
Vol 17 (2) ◽  
pp. 21-27 ◽  
Author(s):  
Kunbin Qu ◽  
Nan Lin ◽  
Yanmei Lu ◽  
Donald Payan
Keyword(s):  
Data Integration ◽  
Multidimensional Data ◽  
Multidimensional Data Integration

Columba: Multidimensional Data Integration of Protein Annotations

2004 ◽  
pp. 156-171 ◽  
Author(s):  
Kristian Rother ◽  
Heiko Müller ◽  
Silke Trissl ◽  
Ina Koch ◽  
Thomas Steinke ◽  
...  
Keyword(s):  
Data Integration ◽  
Multidimensional Data ◽  
Multidimensional Data Integration

scholarly journals Mergeomics: multidimensional data integration to identify pathogenic perturbations to biological systems

BMC Genomics ◽  
2016 ◽  
Vol 17 (1) ◽  
Author(s):  
Le Shu ◽  
Yuqi Zhao ◽  
Zeyneb Kurt ◽  
Sean Geoffrey Byars ◽  
Taru Tukiainen ◽  
...  
Keyword(s):  
Data Integration ◽  
Biological Systems ◽  
Multidimensional Data ◽  
Multidimensional Data Integration

Multidimensional data integration and relationship inference

2002 ◽  
Vol 17 (2) ◽  
pp. 21-27
Author(s):  
Kunbin Qu ◽  
Nan Lin ◽  
Yanmei Lu ◽  
D.G. Payan
Keyword(s):  
Data Integration ◽  
Multidimensional Data ◽  
Multidimensional Data Integration

scholarly journals A comprehensive resource for retrieving, visualizing, and integrating functional genomics data

2019 ◽  
Vol 3 (1) ◽  
pp. e201900546
Author(s):  
Matthias Blum ◽  
Pierre-Etienne Cholley ◽  
Valeriya Malysheva ◽  
Samuel Nicaise ◽  
Julien Moehlin ◽  
...  
Keyword(s):  
Functional Genomics ◽  
High Performance ◽  
Large Scale ◽  
Quality Data ◽  
Multidimensional Data ◽  
Online Resource ◽  
Public Data ◽  
User Friendly ◽  
Quality Assessments ◽  
Multidimensional Data Integration

The enormous amount of freely accessible functional genomics data is an invaluable resource for interrogating the biological function of multiple DNA-interacting players and chromatin modifications by large-scale comparative analyses. However, in practice, interrogating large collections of public data requires major efforts for (i) reprocessing available raw reads, (ii) incorporating quality assessments to exclude artefactual and low-quality data, and (iii) processing data by using high-performance computation. Here, we present qcGenomics, a user-friendly online resource for ultrafast retrieval, visualization, and comparative analysis of tens of thousands of genomics datasets to gain new functional insight from global or focused multidimensional data integration.

scholarly journals Oncology In Vivo Data Integration for Hypothesis Generation

2012 ◽  
Vol 9 (2) ◽  
pp. 19-27
Author(s):  
Jia Wei ◽  
Ming Chen
Keyword(s):  
Animal Model ◽  
Data Integration ◽  
Meta Analysis ◽  
In Vivo Studies ◽  
Multidimensional Data ◽  
Molecular Characteristics ◽  
Administration Routes ◽  
Combination Treatments ◽  
Drug Scheduling

Summary AstraZeneca’s Oncology in vivo data integration platform brings multidimensional data from animal model efficacy, pharmacokinetic and pharmacodynamic data to animal model profiling data and public in vivo studies. Using this platform, scientists can cluster model efficacy and model profiling data together, quickly identify responder profiles and correlate molecular characteristics to pharmacological response. Through meta-analysis, scientists can compare pharmacology between single and combination treatments, between different drug scheduling and administration routes.

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