ASKE-E Month 7 Milestone Report

Natural language dialogue interaction with EMMAA models

This month we developed a new feature that allows users to directly “chat” with an EMMAA model. The main idea is to make use of the CLARE dialogue system we have previously developed, and create custom instances of it, on demand, that load a given EMMAA model and conduct dialogue with respect to that model. An instance of the CLARE system is running on a remote server and can handle multiple independent user sessions simultaneously. Chat sessions are orchestrated through the Pusher framework ( which handles the real-time aspects of the chat interaction (initialize new user session, asynchronously listen to messages, deliver messages to connected clients, etc.). In EMMAA, we implemented a Pusher chat client which integrates into the main EMMAA dashboard. When clicking on the “Chat” button on the card representing a model on the EMMAA dashboard, a new page opens up where the user can put in their email (this is automatically populated if the user is logged in) and start the chat session. They can then talk about a variety of topics, including mechanisms represented in the given EMMAA model.

The image below shows the new “Chat” buttons on the EMMAA dashboard:


The screenshots below show dialogues with two different EMMAA models: the MARM model above and the RAS model below. The first question “what does BRAF interact with?” highlights the fact that in the two sessions, these questions are answered with respect to two different model contexts. In the MARM model, we find that “BRAF can interact with BRAF, RAF1, KRAS, MAPK1, and vemurafenib”, whereas in the RAS model, we find that “BRAF can interact with MAP2K1, SRC, KRAS, and BAD”.

Chat with the MARM model:


Chat with the RAS model:


A key feature of human-machine dialogue as implemented by CLARE is that it maintains dialogue context and can interpret and answer follow-up questions using co-references that refer to previous questions or answers. This allows exploring complex mechanisms, such as ones represented by EMMAA models, sequentially. This kind of sequential exploration with intuitive co-reference resolution would be difficult to implement using traditional form-based web interfaces.

The two dialogues above also demonstrate this context-aware co-reference resolution feature. For instance, the questions “are any of those small molecules?”, “are any of those kinases?” or “are there any drugs for any of those?” are all questions making use of this feature.

We plan to improve the rendering of some answers (bulleted lists, HTML formatting, etc.) in the coming weeks. We will also improve session management on the back-end to allow terminating sessions explicitly thereby freeing up resources. Finally, we plan to make more tutorials and demos available for this dialogue integration to help users make best use of it.

Automatically generated text annotations in context

We implemented a new integration with the that allows taking statements extracted from a given paper, and annotating the website for that paper (a PubMed or PubMed Central landing page, or publisher-specific page) with these statements. First, we implemented an approach to deriving annotation objects from statements. Each annotation consists of a URI (i.e., the address of the page to be annotated), annotation text (i.e., the actual content of the annotation), and a target (a specific text span on the web page that the annotation applies to). The annotation text represets a human-readable English sentence derived from the statement with the names of entities rendered as links to outside ontologies representing them. The target of the annotation is the evidence sentence from which the statement was originally extracted. We can then use the API, for which we implemented a new and extended client, to upload these annotations on demand for a given paper.

We then integrated with new feature with EMMAA. As an extension of the paper-centered view of model statements reported last month, we added a new “ button” which allows annotating a given paper on demand and then looking at the annotations in the context of the actual paper. The figure below illustrates the relevant part of the updated “Paper” tab on the EMMAA dashboard.


For each paper from which statements were extracted, a small hypothesis (“h.”) badge is now displayed. Clicking on this badge starts the process of uploading the annotations for statements extracted from this paper. After all annotations are added, an external page with this paper opens up in a new tab. In addition, a link to this page is displayed on the EMMAA website.


Viewing the uploaded annotations requires the user to install the extension in their browser. The figure below shows how annotations can be viewed and edited on the newly opened page. In this example, a paper on PubMed Central was automatically annotated. The sentences supporting each of the extracted statements are highlighted in the paper and the statements can be viewed in the annotations panel on the right. For instance, this image shows the highlighted sentence mentioning “FGF1–heparin complex” and the extracted “heparin binds FGF1” INDRA statement.


Currently, these annotations are only visible by members of a closed group on, however, we have requested that make annotations in the group publicly visible, and hope that this will be done soon.

Demonstrations at the stakeholder meeting

The February 2021 stakeholder meeting focused on system integration: we demonstrated how EMMAA models can be displayed and interacted with in the HMI developed by Uncharted. First, we showed how a keyword search for an entity of interest can lead a user to “discover” a relevant paper and then an EMMAA model which contains mechanisms surrounding the given entity. The user can then interact with a network view of the model, highlighting interactions derived from the paper of interst in the context of all concepts organized by their ontological categories (for instance, a search for IL6 connects the node representing it in the “Human proteins” category with the node representing SARS in the “Infections” category). The HMI is also able to visualize the subnetwork corresponding to the specific paper on a separate tab. The user can then click on a node to see additional incoming or outgoing interactions and click ont them to add them to this view. The figure below shows interactions highlighted in the context of ontology-based categories on the left, and the separate view of interactions derived from a given paper on the right.


We also showed how the results of model queries can be displayed in the HMI. Here we focused on small molecules that can inhibit the replication of SARS-CoV-2 through an intermediary of interest: the Nrf-2 (NFE2L2) protein. Based on the ontology-guided grouping, the HMI provides an intuitive overview of what types of entties are on each mechanistic path from a drug to SARS-CoV-2. For instance, sildenafil, which is grouped under “vasodilator agents” is shown to regulate the activity of NFE2L2 which in turn can regulate SARS-CoV-2 replication. We also showed examples of drugs inhibiting SARS-CoV-2 via cathepsins. The figure below shows mechanisms by which drugs regulate SARS-CoV-2 via NFE2L2. More detail can be seen by zooming and panning in the HMI.


Developing the EMMAA REST API for flexible integration

We continued working on extending the EMMAA REST API to support integration with other teams. One of the key goals was to allow dynamic retrieval of EMMAA models and tests metadata. To enable this, we implemented four new endpoints in the EMMAA REST API that support the retrieval of the following data:

  • A list of all available EMMAA models;

  • Model metadata (short name, human readable name, description, links to the NDEx landing page and to the model’s Twitter account) for a given model;

  • A list of test corpora that a given model is tested against;

  • Test corpus metadata (name and description) for a given test corpus.

Another important extension of the EMMAA API we implemented is the support for running queries programmatically. Previously it was only possible to submit queries through a web form on the Query page of the EMMAA dashboard and then browse the displayed results. The new approach allows sending programmatic requests to the API and receive the results in JSON format. Similar to the interactive interface on the dashboard, the programmatic endpoint supports three types of queries: static (find directed paths between two entities), open search (find upstream regulators or downstream targets of an entity), and dynamic (confirm dynamical model properties by simulating the model) queries.