Overview
Biological knowledge is increasingly standardized and made available through simple programming interfaces. But how can scientists best consume that information, and use it to enhance their research? ARAGORN (Autonomous Relay Agent for Generation of Ranked Networks) is a tool that assembles information relevant to a user query, and uses that information to synthesize well-supported answers. ARAGORN is a component of the [link to: NCATS Data Translator], and is the evolution of the [link to: ROBOKOP] knowledge-graph– based question- answering system.
RENCI’s Role
Light sheet fluorescence microscopy images can shed new light on the understanding of three dimensional structures of the whole brain by allowing researchers to localize and characterize individual brain cells. To reduce the need for manual annotation and speed scientists’ ability to gain insights from these images, RENCI is designing a semi-automated annotation software and crowd-sourcing platform for annotations that will generate high-quality training data for a 3D cell segmentation engine capable of annotating new microscopy images automatically. Scientists will apply this computational tool to several pilot studies including Autism mouse brain and human fetal tissue data.
Project Team
- David Borland (RENCI Lead)
Partners
- University of North Carolina at Chapel Hill Department of Psychiatry
- University of North Carolina at Chapel Hill Department of Genetics
Funding
- NIH
