Machine Reasoning And Visualization Based On Example Of Glial-Synapse Interplay Throughout Inflammation Dynamics
We utilized machine learning methods to match complementing facts from different papers. The algorithm generates single reasoning chain to hypothesize description of the biological process with the highest possible level of detalization. The reasoning module is a part of the sci.AI platform and analyzes lexical and biological groups of features of the tuples that are semantically extracted from the original research papers. The inferred pathway is visually encoded with spatial (histological) and timeline dimensions. It is done with the aim of communicating the process as close as possible to biological ground truth.
Here we present performance of the
machine method based on the example of inferred inflammatory cascade
and glial-synapse interactions.
The Poster was presented at Federation of European Neuroscience Societies (FENS) Forum of Neuroscience, Berlin, Germany, 2018