Stottler Henke is developing PACT, through the use of artificial intelligence (AI) / machine learning (ML) technologies, to address the threat posed by unknown/novel bacteria, in order to assess its pathogenic potential for DARPA’s Biological Technologies Office. Threat assessment is inferred from phenotype as characterized by a series of assays developed by Harvard University as part of DARPA’s Friend or Foe program, designed specifically to target various aspects of a bacteria’s phenotype that are heavily correlated with pathogenicity. These phenotypic features captured in the data include: cellular growth, viability in the presence of different media, niche finding, immune avoidance, & cytotoxicity. Based on the results of previous work, we expect our model will be able to learn a better decision boundary from this carefully curated feature set.
The PACT system features a novel semi-supervised neural architecture that is capable of learning from both labeled and unlabeled data. This concept of using an unsupervised task to improve neural network performance has driven an entire thread of neural network research leading to state-of-the-art performance on various tasks. The ultimate goal for this technology is to improve national security by furthering the state-of-the-art in bio surveillance/biodefense technology.
Disclaimer: This material is based upon work supported by the Defense Advanced Research Projects Agency (DARPA) under Contract No. 140D0420C0019. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Defense Advanced Research Projects Agency (DARPA); or its Contracting Agent, the U.S. Department of the Interior, Interior Business Center, Acquisition Services Directorate, Division III.