Knowledge Engineering Enabled Neuroscience (KEEN)
We seek to provide a service for experimental neuroscientists by providing made-to-order, modular, principled, open-source knowledge engineering neuroinformatics software based on an agile, rapid-deployment model.
Experimental neuroscientists generate data over a wide range of modalities, under complex designs and for a wide range of purposes. Developing knowledge engineering tools to enable management, analysis and publishing of this data is challenging for a neuroscientist PI running a lab since (a) interpreting the data requires specialized neuroscience knowledge and (b) developing these tools require expert software engineering work. Thus, even though neuroscience PIs may want (and even need) neuroinformatics tools to showcase, demonstrate, and analyse their data, the process of developing them has, until now, proven prohibitive and impractical. External work-for-hire developers lack the required neuroscience expertise. Off-the-shelf solutions typically do not exist for the niche-requirements of individual labs and open-source implementations are likely to have been built by graduate students and are unlikely to work well outside of their originating laboratories.
This work leverages a code-generation methodology to construct 'scaffolding' for a complete web-application based on models of the desired functionality that we develop in close collaboration with scientists. This permits us to build prototypes which can be practically deployed rapidly and further tailored to the needs of the sponsoring laboratory. Our goal is to make the process of developing these tools as rapid, straightforward and predictable as possible. We foster best code development practices (unit testing, user stories, documentation, advanced dependency management, one-click installable deployment, evaluation based on maturity models etc.). All code will be published on Github for easy reuse and adoption by the broader neuroinformatics community.
In addition, beyond the initial support of building simple data management and analysis and we can provide additional support to leverage cutting edge AI-driven research in collaboration with colleagues at ISI. This includes: (A) text mining full-text articles of interest; (B) application of reproducible workflow analysis technology; (C) integration across existing data sources; (D) use of reasoning systems, ontologies and semantic web technology.
This effort will be lead by Gully Burns (http://www.isi.edu/people/burns/homepage). As a professional neuroinformatics specialist (D.Phil. from Oxford, 1997), Gully has over 15 years of experience developing software tools for neuroscientists and sees this a novel strategy to increase the effectiveness and impact of neuroinformatics systems in supporting bench scientists in their research.
Here we showcase the NeuARt system developed in collaboration with Larry Swanson's group at USC to manage and make available complex neural connectivity maps that are painstakingly assembled in the Swanson lab. This is an example of the sort of systems that we build for use by bench scientists.
If you are a neuroscientist with data that you need to convert into a well-defined neuroinformatics application for use in your lab or the broader community, we are available to work as subcontractors on grants. We also provide access to advanced functionality based on our interactions with colleagues in the AI community. Email Gully Burns for more details.