ROBOKOP KG
ROBOKOP KG is an open-source biomedical knowledge graph containing ~10M nodes and ~250M edges drawn from ~30 biological data sources and bio-ontologies. Query the ROBOKOP KG directly in the Neo4j browser.
AIxB
artificial intelligence and the biological Sciences
Biology and Artificial Intelligence (AI) have long had a symbiotic relationship. Some early efforts in AI were modeled on biological processes, most notably genetic algorithms and neural networks; but as the applications of AI have expanded, the overt relationship between the ideas behind Biology and AI has been obscured. Our teams seeks to re-invigorate this symbiosis by employing innovative AI approaches that incorporate the unifying principles of biology to tackle a challenging biological problems. For instance, scientists have tried for over a century to decipher how the information encoded in the DNA of an organism gives rise to its consequential constellation of observable traits. This “genotype to phenotype” problem has been painstakingly investigated by manipulating individual genes—using natural variation, chemical or radiation induced mutation, or editing with CRISPR and related genome editing technologies—and investigating the resultant phenotypes. While direct and fruitful, this approach is slow and does not scale well. Recently, researchers have poured data from genomes, transcriptomes, proteomes, and the like into databases and other repositories, with the hope that these massive datasets would elucidate how genotype defines phenotype. While rich, these datasets have become impossible for most biologists to synthesize and interrogate fully. AI enables scientists to process unprecedented amounts of data and detect patterns and phenomena that might never have been spotted by the human eye. However, the common approaches use are often black boxes that can describe the data or its patterns but fail to provide biological or mechanistic insights.
Our goal is to jointly address two challenges: developing a better understanding of how to apply AI to biological problems by inventing and implementing novel AI approaches that both predict and explain the relationships revealed and to uses the novel biological mechanisms and patterns discovered to inspire new approaches to AI. Led by Principal Investigator Corbin Jones, professor of biology in the College of Arts and Sciences, AIxB has the potential to revolutionize AI for biosciences by taking a fresh look at AI technologies through the lens of biology.
Events
Welcome to the EpiCenter Webinar and Survey!
Explore the Future of Epigenetics with EpiCenter
Join us for an exclusive webinar where we dive deep into the latest advancements in histone modification analysis and introduce our pioneering community resource, EpiCenter. Learn how this platform is set to transform the landscape of genomic research by providing high-resolution insights into chromatin dynamics and gene regulation.
Webinar Details:
- Topic: Advances in Histone Modification Analysis: Techniques, Technologies, and Applications
- Date: November/December 2024
- Time: TBD
- Duration: 1 hour Max
- Platform: Zoom
What You Will Learn:
- Cutting-edge techniques for histone modification analysis.
- How we integrate these technologies to support groundbreaking research.
- The impact of AI and machine learning on epigenetic research.
- How you can engage with and benefit from the resources we are building.
Who Should Attend:
This webinar is ideal for researchers, academics, and students in genetics, molecular biology, and related fields, as well as anyone interested in the latest developments in epigenetics.
Register Now! Secure your spot [Link to registration] and be part of this enlightening session.Take the Survey Here
Tools
ExEmPLAR
ExEmplar is a generic Neo4j query-and-answer exploration tool that supports queries of the ROBOKOP KG and other Neo4j databases.
ExEmplar is under development and should be considered an experimental alternative to the ROBOKOP UI.
RoboDocumentation
A collection of notebooks and other documents to help users get the most out of ROBOKOP.
Resources
- Resource 1: description description and a link
- Resource 2: with link
Projects
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Funding
ROBOKOP is a joint creation of the Renaissance Computing Institute (RENCI) at the University of North Carolina at Chapel Hill and CoVar LLC. The prototype was developed with funding from the National Center for Advancing Translational Sciences, National Institutes of Health (award #OT2TR002514). ROBOKOP's continued development is supported with joint funding from the National Institute of Environmental Health Sciences and the Office of Data Science Strategy within the National Institutes of Health (award #U24ES035214).
License
ROBOKOP is available under the MIT license.