Date: Tuesday, 13 September 2016, 10 am (Manila Time)


Abstract

With the explosion of data in different dimensions of drug discovery, biomedicine and healthcare, a key challenge is the ability to connect the disparate data sources, discover the right analytics tools for a specific analysis and navigate through inter-operable analytics to provide executable insights.

Garuda is an open, community-driven, platform that provides a framework to discover, connect & navigate through different applications on devices as well as in the cloud. We provide an overview of the Garuda platform, the alliance eco-system and a brief demonstration of the platform in action together with its various components. Specifically, we focus on case studies of the application of Garuda platform in drug discovery pipelines – in discovery research, translational research as well as analysis of clinical data. We demonstrate a case study of how different sensors, wearables and monitoring devices are connected on Garuda to aggregate signals, analyze data-points and identify patterns to enable decision-making.

We summarize with a vision on re-imagining connectivity in the age of digital medicine, healthcare and beyond.


samik_ghoshSamik Ghosh, PhD

Senior Scientist, The Systems Biology Institute, Tokyo
CTO, SBX Corp.
President and CEO, SBX Technologies Corp.
http://www.sbi.jp/
http://www.garuda-alliance.org/

About the Speaker

Extensive background in networks and telecommunication, modeling in systems biology, drug discovery, software development and start-up experience in USA. He is also a Senior Researcher at The Systems Biology Institute, Tokyo and serves as the CTO of SBX Corporation leading the software development effort of computational platform (Ghosh et.al, Nature Reviews Genetics 2011), device consultancy and drug discovery pipeline consultancy. He received his Bachelors in Technology from India in 2001, MS and PhD in Computer Sciences from The University of Texas at Arlington in 2004 and 2007 respectively, where his research focused on computational modeling of biological systems.

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