Spatiotemporal integration of molecular and anatomical data in virtual reality using semantic mapping.
We have developed a computational framework for spatiotemporal integration of molecular and anatomical datasets in a virtual reality environment. Using two case studies involving gene expression data and pharmacokinetic data, respectively, we demonstrate how existing knowledge bases for molecular data can be semantically mapped onto a standardized anatomical context of human body. Our data mapping methodology uses ontological representations of heterogeneous biomedical datasets and an ontology reasoner to create complex semantic descriptions of biomedical processes. This framework provides a means to systematically combine an increasing amount of biomedical imaging and numerical data into spatiotemporally coherent graphical representations. Our work enables medical researchers with different expertise to simulate complex phenomena visually and to develop insights through the use of shared data, thus paving the way for pathological inference, developmental pattern discovery and biomedical hypothesis testing.
Sun Center of Excellence for Visual Genomics, University of Calgary, Calgary, AB, Canada.
Jung
J
Soh
Andrei L
AL
Turinsky
Quang M
QM
Trinh
Jasmine
J
Chang
Ajay
A
Sabhaney
Xiaoli
X
Dong
Paul Mk
PM
Gordon
Ryan Pw
RP
Janzen
David
D
Hau
Jianguo
J
Xia
David S
DS
Wishart
Christoph W
CW
Sensen
D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D008953:Models, Anatomic; D008954:Models, Biological; D010599:Pharmacokinetics; D020543:Proteome / Q000378:metabolism*; D014018:Tissue Distribution; D014584:User-Computer Interface
D020543:Proteome