ContentMining for Synthetic Biology

Nov. 24, 2015
ContentMining for Synthetic Biology
ContentMining for Synthetic Biology
ContentMining for Synthetic Biology
ContentMining for Synthetic Biology
ContentMining for Synthetic Biology
ContentMining for Synthetic Biology
ContentMining for Synthetic Biology
ContentMining for Synthetic Biology
ContentMining for Synthetic Biology
ContentMining for Synthetic Biology
ContentMining for Synthetic Biology
ContentMining for Synthetic Biology
ContentMining for Synthetic Biology
ContentMining for Synthetic Biology
ContentMining for Synthetic Biology
ContentMining for Synthetic Biology
ContentMining for Synthetic Biology
ContentMining for Synthetic Biology
ContentMining for Synthetic Biology
ContentMining for Synthetic Biology
ContentMining for Synthetic Biology
ContentMining for Synthetic Biology
ContentMining for Synthetic Biology
ContentMining for Synthetic Biology
ContentMining for Synthetic Biology
ContentMining for Synthetic Biology
ContentMining for Synthetic Biology
ContentMining for Synthetic Biology
ContentMining for Synthetic Biology
ContentMining for Synthetic Biology
ContentMining for Synthetic Biology
ContentMining for Synthetic Biology
ContentMining for Synthetic Biology
ContentMining for Synthetic Biology
ContentMining for Synthetic Biology
ContentMining for Synthetic Biology
ContentMining for Synthetic Biology
ContentMining for Synthetic Biology
ContentMining for Synthetic Biology
ContentMining for Synthetic Biology
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ContentMining for Synthetic Biology

Editor's Notes

  1. Hi, I’m here to talk about AMI; a data extraction framework and tool. First, I just want highlight some of key contributors to the projects; Andy for his work on the ChemistryVisitor and Peter for the overall architecture. In this talk, I’m going to impress the importance of data in a specific format and its utility to automated machine processing. Then I’m going to demonstrate AMI’s architecture and the transformation of data as it flows through the process. I’m going to dwell a little on a core format used, Scalable Vector Graphics (SVG) before introducing the concept of visitors, which are pluggable context specific data extractors. Next, I’m going to introduce Andy’s ChemVisitor, for extracting semantic chemistry data, along with a few other visitors that can process non-chemistry specific data. Finally, I will demonstrate some uses of the ChemVisitor, within the realm of validation and metabolism.
  2. Because information is structured (some examples listed), we can aggregate similar objects and mine using a modular systematic approach.
  3. Because information is structured (some examples listed), we can aggregate similar objects and mine using a modular systematic approach.
  4. Can describe each collaboration, but keep this slide brief if the presentation is short.