ASKE-E Month 2 Milestone Report¶
Push science: EMMAA models tweet new discoveries and explanations¶
This month we implemented and deployed Twitter integration for multiple EMMAA models. We have previously developed a proof of concept for Twitter integration, however, that framework had significant limitations. First, tweets only described structural updates to a model (i.e., the number of new statements that were added) and did not report on any functional changes or non-trivial new insights that were gained from the model update. Second, the tweets did not point to any landing page where users could examine the specific changes to the model. In the new Twitter integration framework, we addressed both of these crucial limitations.
Twitter updates are now generated for three distinct types of events triggered by the appearance of new discoveries in the literature:
New (note that “new” here means that a statement is meaningfully distinct from any other statement that the model previously contained) statements added to a model.
The model becoming structurally capable to make a set of new explanations with respect to a set of tests (e.g., experimental findings). This typically happens if a new entity is added to the model that was previously not part of it.
The model explaining a previously unexplained observation (in other words, passing a previously failing “test”). These notifications are particularly important conceptually, since they indicate that the model learned something from the newly ingested literature that changed it such that it could explain something it previously couldn’t.
The image below shows the first tweet from the [EMMAA COVID-19 model](https://twitter.com/covid19_emmaa).
Crucially, each of the tweets above include a link to a specific landing page where the new results can be examined and curated (in case there are any issues).
Overall, this framework constitutes a new paradigm for scientists to monitor the evolving literature around a given scientific topic. For instance, scientists who follow the EMMAA COVID-19 model Twitter account get targeted updates on specific new pieces of knowledge that were just published that enable new explanations of drug-virus effects.
Improving named entity recognition in text mining integrated with EMMAA models¶
Having evaluated the performance of integrating protein cleavage product names from the Protein Ontology with the Reach reading system’s resources, we found that the space of protein fragments covered and the quality of synonyms was insufficient. We therefore implemented an alternative approach that involves extracting protein chain and fragment names from UniProt and using these as synonyms for grounding purposes (see [Pull request](https://github.com/clulab/bioresources/pull/42)). We found that this approach adds around 50 thousand new, high-quality lexicalizations for protein fragments, including a large number of human proteins (e.g., Angiotensin-2) and viral proteins (e.g., Nsp1) that are of interest for COVID-19 and many other applications in biology. The UA team is currently working on finalizing these updates and we hope to run an updated version of Reach on the COVID-19 literature next month.
Making model tests and paths available for use by other applications¶
To facilitate integration of EMMAA test results with other applications we made data on model tests and causal paths available for programmatic download. This feature was requested by the Uncharted team, who is exploring approaches to visualize and interact with EMMAA results. The test and path data are stored in public JSON-L files on Amazon S3 and are updated daily. Model test files contain a JSON representation of the EMMAA test statements; test path files list the path nodes, the statement hashes supporting each edge in the path, the hash of the corresponding test, and the type of causal network used to evaluate the test. Downstream applications can get the latest results from each model-test corpus pair from stable Amazon S3 links.