Big Data Analytics to Advance Stroke and Cerebrovascular Disease: a tool to bridge translational and clinical research

42.6K

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250

authors

26

articles

Big Data Analytics to Advance Stroke and Cerebrovascular Disease: a tool to bridge translational and clinical research

42.6K

views

250

authors

26

articles

Editors

3

Alexis Netis Simpkins

University of Florida

Hari Kishan Reddy Indupuru

University of Texas Health Science Center at Houston

Sean Isaac Savitz

University of Texas Health Science Center at Houston

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About

Stroke is a complex condition with many confounders that can influence patient outcomes, such as age, medications, diet, and stroke phenotype. One way of controlling for the large variances that can occur is to use very large data sets such that the contribution of these confounders can be studied and controlled for when appropriate. Big data research in stroke includes evaluations of large clinical and biological signals associated with stroke outcome measures. Using big data can refine decision-making regarding acute stroke therapy and optimize stroke prevention therapy using a patient approach.

One useful Big Data approach in stroke research has been combining clinical data such as imaging and clinical characteristics with biological data such as RNA sequencing and genomics to discover novel therapeutic targets or clinical disease modifiers. Our goal is to present examples of effective utilization of Big Data approaches of combined clinical, imaging, and biologic analyses that advance stroke research development for stroke patients. A few examples demonstrating the utility of this approach include insightful discoveries of the genetic predispositions for cerebrovascular disease like small vessel disease and stroke in the young, systems biology of penumbral tissue during acute stroke, improving the diagnostic accuracy of stroke testing, and identifying novel drug targets.

To address these scientific challenges, we encourage article submissions that demonstrate the utility of a combined clinical, imaging, and biological big data approaches that address the following topics:

• Acute stroke therapies

• Secondary stroke prevention

• Rehabilitation post-stroke

• Primary prevention and stroke

• Socioeconomic contributors to stroke pathophysiology and outcomes

• Genetic and transcriptomic changes associated with stroke outcomes and pathophysiology

• Stroke registries

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Editors

Alexis Netis Simpkins

University of Florida

Hari Kishan Reddy Indupuru

University of Texas Health Science Center at Houston

Sean Isaac Savitz

University of Texas Health Science Center at Houston

Impact

42,629 Total views

28,562 Article views

13,377 Article downloads

690 Topic views

Published In

Frontiers in Neurology

Stroke

Frontiers in Big Data

Medicine and Public Health

Frontiers in Artificial Intelligence

Medicine and Public Health

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