Various ‘omics technologies produce large data sets that can be difficult to sift through in search of insights. In 2018 the CompBio platform was invented to facilitate true multi-omic analysis based on a novel approach, known as Contextual Language Processing (CLP), that employs the entirety of PubMed abstracts and millions of full-text papers. Combining CLP with other unique methods enabled the building of thousands of holistic and interconnected “knowledge maps”. Users’ input data are then contextualized with this novel presentation of the biomedical corpus.
Over the last two years, we have developed a far more powerful version of the platform based on augmented artificial intelligence (AAI) that combines PCMM, a novel multi-dimensional memory creation and retrieval system, with Assertion Engine, a machine learning system for generalized pattern detection. One of the key aspects of the new suite of tools built on the AAI engine is the ability to, both quantitatively and qualitatively, compare human disease profiles with animal models and drug profiles. Professor Head, chief inventor, will discuss the conceptual design of the AAI engine, improvements in features and usability, distinction from traditional pathway tools, major publications, and an effort to create large-scale human disease comparative profiles for translational research.