This website describes projects that are led by Benjamin M. Gyori at Northeastern University since August 2023. Previously, the research group was based at the Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science (HiTS), at Harvard Medical School.


Our group is developing systems that can accelerate scientific discovery in biomedicine using a combination of text mining, knowledge assembly, mathematical modeling and causal analysis. We are pursuing approaches that use artificial intelligence to increasingly automate the interpretation of scientific literature and large experimental datasets, and also to enable sophisticated human-machine interaction and collaboration. Applications of these tools range from drug discovery for cancer and other diseases to modeling complex processes at the interface of physical and social systems.




INDRA (Integrated Network and Dynamical Reasoning Assembler) is an automated model assembly system that uses natural language processing and structured databases to collect mechanistic and causal assertions, represent them in a standardized form, and assemble them into causal graphs or dynamical models. Internally, INDRA performs knowledge assembly to correct errors, find and resolve redundancies, infer missing information, filter to a scope of interest and assess belief.
Code   Docs   REST API



EMMAA (Ecosystem of Machine-maintained Models with Automated Analysis) monitors the scientific literature for new findings and automatically updates a set of disease-specific models with new knowledge. It also automatically analyzes these models against a set of test conditions (typically experimental observations) and measures the effect of new knowledge on these results. It then notifies users about relevant new analysis results.
Website   Code   Docs   REST API

INDRA Biomedical Discovery Engine

INDRA Biomedical Discovery Engine

The INDRA Biomedical Discovery Engine is an integrated web portal that builds around an automatically assemble knowledge graph combining causal mechanisms assembled by INDRA with relations repesenting ontologies and data. It supports uploading user data to run analysis as well as the browsing and curation of different facets of biomedical knowledge. The website also contains an integrated human-machine dialogue system to sequentially explore the knowledge graph.
Website   Code

INDRA Database

The INDRA Database makes knowledge assembled by INDRA at scale available as a service. It aggregates knowledge extracted by multiple machine-reading systems from all available abstracts and open-access full text articles, and combines this with mechanisms from pathway databases. Queries allow searching for genes, chemicals, biological processes and other concepts of interest, and returns a ranked list of relevant interactions.
Website  REST API   Code

INDRA-IPM (Interactive Pathway Map)

The INDRA-IPM allows you to build pathway maps using natural language descriptions. You simply describe the set of mechanisms to include in English, and then click a button to assemble and lay out a pathway map. The pathway can be exported into various formats like SBML, SBGN, Kappa and others.
Website   Code

Bob with Bioagents is a machine assistant you can chat with about molecular biology. Assume you want to explain an experimental observation, or get some ideas for a new hypothesis. You can talk with the machine agent in English language to discuss topics such as drugs, transcription factors, miRNAs, and their targets, and various mechanisms described in the literature and databases. You can also build up a model of a mechanism during the dialogue, and then ask questions about the properties of the model to see if it behaves as expected.

CLARE machine assistant

CLARE is a machine assistant that can be deployed as a Slack application in a workspace and engage in human-machine dialogue in channels and private messages. It can answer questions about mechanisms such as "what phosphorylates ELK1?" or "does RHOA interact with MYL12B?" and connect this information to other resources, while also allowing to build and discuss models of mechanisms. Please contact us if you'd like to deploy CLARE on your Slack workspace.
Demo video


In the context of the DARPA World Modelers program, we have generalized INDRA to modeling complex causal mechanisms governing processes such as agricultural production, food security and migration. We are also part of the DSMT-E project, a large collaboration funded by DARPA and the Gates Foundation to develop AI-driven decision support tools for Ethiopia.
Code   DSMT-E

Network search and DepMap explainer

The INDRA Network search builds on the INDRA assembly of literature extractions and pathway databases to find mechanistic paths between entities of interest. It allows searching for a variety of patterns (paths between, common up/downstreams, etc.) and tuning multiple search parameters to define constraints and context. The DepMap explainer is a specific instance of network search aimed at constructing explanations for correlations between genes involved in CRISPR screens of cancer cell lines found at
DepMap explainer   Network search

Application to COVID-19

In the context of the ongoing COVID-19 pandemic, the INDRA team is working on understanding the mechanisms by which SARS-CoV-2 infects cells and the subsequent host response process, with the goal of finding new therapeutics using INDRA.

Application to studying pain and inflammation

In the context of the DARPA Panacea program, the INDRA team is working on understanding the regulation of pain and inflammation with the goal of finding new therapeutics using INDRA.


Adeft (Acromine based Disambiguation of Entities From Text context) builds machine-learning models to disambiguate acronyms and other abbreviations of biological terms in the scientific literature. A growing number of pretrained disambiguation models are publicly available through the Python package.


Gilda is a Python package and REST service that grounds (i.e., finds appropriate identifiers in namespaces for) named entities in biomedical text. It also uses a set of machine-learned disambiguation models to choose between different senses of ambiguous synonyms. It can be integrated into applications as a Python package or through the REST service.
Website and REST API   Code

Biopragmatics tools

Biopragmatics stack

We developed the Biopragmatics stack, a collection of interlinked software packages that provide infrastructure for working with biomedical ontologies and their entries. They include the Bioregistry (a meta-registry of biomedical nomenclatures), Bioversions (a service for tracking versions of biomedical resources), Biomappings (community curated equivalences across biomedical ontologies), Biolookup (a service for retrieving metadata about biomedical entities), and PyOBO (a software package to facilitate processing biomedical ontologies in a unified fashion).
Website Code

Projects and Funding

Big Mechanism The DARPA Big Mechanism program set out to automate the reading, assembly and modeling of mechanisms from the scientific literature. We built INDRA, an automated model assembly system which draws on natural language processing systems, and assembles their output into various predictive and explanatory models.
Funded by the Defense Advanced Research Projects Agency under award W911NF-14-1-0397 (2014-2019).

Communicating with Computers The DARPA Communicating with Computers (CwC) program develops technologies for a new generation of human-machine interaction in which machines act as proactive collaborators rather than merely problem solving tools. We are developing an interactive dialogue system which allows scientists to interact with a computer partner – one that is able to harness knowledge extracted from the biomedical literature – to construct and test hypotheses about molecular systems.
Funded by the Defense Advanced Research Projects Agency under award W911NF-15-1-0544 (2015-2021).

Automated Scientific Discovery Framework The DARPA Automated Scientific Discovery Framework program (ASDF) will develop algorithms and software for reasoning about complex mechanisms operating in the natural world, explaining large-scale data, assisting humans in generating actionable, model-based hypotheses and testing these hypotheses empirically.
Funded by the Defense Advanced Research Projects Agency under award W911NF018-1-0124. (2018-2020)

World Modelers The DARPA World Modelers program aims to develop automated information collection and computational modeling techniques to understand the complex dynamics of global processes such as food security, migration and public health. We are developing the INDRA-GEM (Integrated Network and Dynamical Reasoning Assembler for Generalized Ensemble Modeling) automated model assembly system, which integrates information from diverse sources and implements novel probabilistic assembly techniques that can account for the uncertain nature of information in models.
Funded by the Defense Advanced Research Projects Agency under award W911NF-18-1-0014. (2017-2022)

Automating Scientific Knowledge Extraction The DARPA ASKE program is part of DARPA's broader Artificial Intelligence Exploration program with the goal of developing technologies for the "Third Wave" of AI. We are developing EMMAA (Ecosystem of Machine-maintained Models with Automated Assembly), a set of self-updating models of cancer biology that run analysis proactively, and report about meaningful changes in conclusions to users.
Funded by the Defense Advanced Research Projects Agency under award HR00111990009. (2018-2021)

Panacea The STOP PAIN project, as part of DARPA’s Panacea program, aims to develop novel drugs for the treatment of pain and inflammation using innovative research platforms. Unlike many modern drug discovery campaigns, which are target focused, we combine target-agnostic screening with network inference tools to create causal and mechanistic networks used for identification of previously unknown target-chemical ligand relationships.
Funded by the Defense Advanced Research Projects Agency under award HR00111920022. (2019-2023)

DARPA Young Faculty Award and Director's Fellowship Award Benjamin M. Gyori received a DARPA Young Faculty Award for 2020-2022 for the project "Collaborative scientific discovery with semantically linked, machine built models". The project aims to automate the construction of models of complex mechanisms embedded in knowledge graphs, and build automated analysis and human-machine collaboration capabilities around this to enable rapid scientific discovery. Due to the initial success of the project, Ben was awarded the DARPA Director's Fellowship Award for 2022-2023 to continue the project.
Funded by the Defense Advanced Research Projects Agency under award W911NF2010255. (2020-2023)

Automating Scientific Knowledge Extraction and Modeling (ASKEM) The DARPA ASKEM program aims to develop technologies to significantly speed up how experts create, maintain and analyze models of complex processes such as viral pandemics and space weather. We are developing the MIRA system which is a framework for representing systems using ontology-grounded meta-model templates, and generating various model implementations and exchange formats from these templates. It also implements algorithms for assembling and querying domain knowledge graphs in support of modeling.
Funded by the Defense Advanced Research Projects Agency under award HR00112220036. (2022-)

Rapid Assessment of Platform Technologies to Expedite Response (RAPTER) The RAPTER program aims to create a machine-learning tool to revolutionize the future of vaccine development, rapidly choosing a suitable vaccine platform for any viral and bacterial pathogen. Within RAPTER, we are developing text mining, knowledge assembly and knowledge graph technology to create an actionable repository of vaccine/pathogen knowledge from which models can be derived.
Funded by the Defense Threat Reduction Agency. (2023-)


Note that these publications are ones specific to the team's projects. Gyori's full publication list is available on Google Scholar.


Published Gyori BM, Bachman JA, Subramanian K, Muhlich JL, Galescu L, Sorger PK. From word models to executable models of signaling networks using automated assembly. Molecular Systems Biology, 2017 13(11):954.
Summary video:


Published Bachman JA, Gyori BM, Sorger PK Automated assembly of molecular mechanisms at scale from text mining and curated databases Molecular Systems Biology, e11325, 2023.

Published Hoyt, C. T., Hoyt, A. L., and Gyori, B. M. Prediction and Curation of Missing Biomedical Identifier Mappings with Biomappings Bioinformatics, 39(4), btad130, 2023.

Published Pillich RT, Chen J, Churas C, Fong D, Gyori BM, Ideker T, Karis K, Liu SN, Ono K, Pico A, Pratt D NDEx IQuery: a multi-method network gene set analysis leveraging the Network Data Exchange Bioinformatics, 39(3), btad118, 2023.

Published Lobentanzer S, Aloy P, Baumbach J, Bohar B, Danhauser K, Doğan T, Dreo J, Dunham I, Fernandez-Torras A, Gyori BM, Hartung M, Hoyt CT, Klein C, Korcsmaros T, Maier A, Mann M, Ochoa D, Pareja-Lorente E, Preusse P, Probul N, Schwikowski B, Sen B, Strauss MT, Turei D, Ulusoy E, Wodke J, Saez-Rodriguez J Democratising Knowledge Representation with BioCypher Nature Biotechnology, 2023.

Preprint Jain A, Gyori BM, Hakim S, Bunga S, Taub DG, Ruiz-Cantero MC, Tong-Li C, Andrews N, PK Sorger, CJ Woolf Nociceptor neuroimmune interactomes reveal cell type- and injury-specific inflammatory pain pathways. bioRxiv, 2023

Preprint Tiffany J. Callahan, Ignacio J. Tripodi, Adrianne L. Stefanski, Luca Cappelletti, Sanya B. Taneja, Jordan M. Wyrwa, Elena Casiraghi, Nicolas A. Matentzoglu, Justin Reese, Jonathan C. Silverstein, Charles Tapley Hoyt, Richard D. Boyce, Scott A. Malec, Deepak R. Unni, Marcin P. Joachimiak, Peter N. Robinson, Christopher J. Mungall, Emanuele Cavalleri, Tommaso Fontana, Giorgio Valentini, Marco Mesiti, Lucas A. Gillenwater, Brook Santangelo, Nicole A. Vasilevsky, Robert Hoehndorf, Tellen D. Bennett, Patrick B. Ryan, George Hripcsak, Michael G. Kahn, Michael Bada, William A. Baumgartner Jr, Lawrence E. Hunter An Open-Source Knowledge Graph Ecosystem for the Life Sciences. arXiv, 2023


Published Hoyt CT, Balk M, Callahan, TJ, Domingo-Fernandez D, Haendel MA, Hegde HB, Himmelstein DS, Karis K, Kunze J, Lubiana T, Matentzoglu N, McMurry J, Moxon S, Mungall CJ, Rutz A, Unni DR, Willighagen E, Winston D, & Gyori BM Unifying the Identification of Biomedical Entities with the Bioregistry Scientific Data, 2022.

Published Gyori BM, Hoyt CT, Steppi A, Gilda: biomedical entity text normalization with machine-learned disambiguation as a service Bioinformatics Advances, 2022.

Published Scholten B, Guerrero Simón L, Krishnan S, Vermeulen R, Pronk A, Gyori BM, Bachman JA, Vlaanderen J, Stierum R. Automated Network Assembly of Mechanistic Literature for Informed Evidence Identification to Support Cancer Risk Assessment Environmental Health Perspectives, 2022.

Published Gyori BM and Hoyt CT. PyBioPAX: biological pathway exchange in Python Journal of Open Source Software, 2022.

Published Balabin H, Hoyt CT, Birkenbihl C, Gyori BM, Bachman JA, Kodamullil AT, Ploeger PG, Hofmann-Apitius M, Domingo-Fernandez D. STonKGs: A Sophisticated Transformer Trained on Biomedical Text and Knowledge Graphs Bioinformatics, 2022.

Published Balabin H, Hoyt CT, Gyori BM, Bachman JA, Kodamullil AT,Hofmann-Apitius M, Domingo-Fernandez D. ProtSTonKGs: A Sophisticated Transformer Trained on Protein Sequences, Text, and Knowledge Graphs SWAT4HCLS, 2022.

Published N Matentzoglu, JP Balhoff, SM Bello, C Bizon, M Brush, TJ Callahan, CG Chute, WD Duncan, CT Evelo, D Gabriel, J Graybeal, A Gray, BM Gyori, M Haendel, H Harmse, NL Harris, I Harrow, H Hegde, AL Hoyt, CT Hoyt, ..., CJ Mungall A Simple Standard for Sharing Ontological Mappings (SSSOM) Database, 2022.

Published Bonner S, Barrett IP, Ye C, Swiers R, Engkvist O, Hoyt CT, Hamilton WL Understanding the Performance of Knowledge Graph Embeddings in Drug Discovery Artificial Intelligence in the Life Sciences, 2022.

Published Doherty LM, Mills CE, Boswell SA, Liu X, Hoyt CT, Gyori BM, Buhrlage SJ, Sorger PK. Integrating multi-omics data reveals function and therapeutic potential of deubiquitinating enzymes eLife 11:e72879, 2022.
Website: DUB portal

Published Mohammad-Taheri S, Zucker J, Hoyt CT, Sachs K, Tewari V, Ness R, Vitek O Do-calculus enables causal reasoning with latent variable models Bioinformatics, 2022.

Published de Crécy-lagard V, de Hegedus RA, Arighi C, ..., Gyori BM, ..., Xu J A roadmap for the functional annotation of protein families: a community perspective Database, 2022.

Published Rozemberczki B, Hoyt CT, Gogleva A, Grabowski P, Karis K, Lamov A, Andriy N, Nilsson S, Ughetto M, Wang Y, Derr T, Gyori BM. ChemicalX: A Deep Learning Library for Drug Pair Scoring KDD, 2022.

Published Hungerford J, Chan YS, MacBride J, Gyori BM, ..., Reynolds M, Surdeanu M, Bethard S, Sharp R Taxonomy Builder: a Data-driven and User-centric Tool for Streamlining Taxonomy Construction NAACL HCI+NLP, 2022.

Published Bonner S, Barrett IP, Ye C, Swiers R, Engkvist O, Bender A, Hoyt CT, Hamilton WL A Review of Biomedical Datasets Relating to Drug Discovery: A Knowledge Graph Perspective Briefings in Bioinformatics, 2022.

Published Matentzoglu N, Goutte-Gattat D, Tan SZK, Balhoff JP, Carbon S, Caron AR, Duncan WD, Flack JE, Haendel M, Harris NL, Hogan WR, Hoyt CT, Jackson RC, Kim H, Kir H, Larralde M, McMurry JA, Overton JA, Peters B, ... Osumi-Sutherland D. Ontology Development Kit: a toolkit for building, maintaining, and standardising biomedical ontologies Database, 2022.

Preprint Mohammad-Taheri S, Tewari V, Kapre R, Rahiminasab E, Sachs K, Hoyt CT, Zucker J, Vitek O Experimental design for causal query estimation in partially observed biomolecular networks arXiv, 2022.

Preprint Bachman JA, Sorger PK, Gyori BM. Assembling a corpus of phosphoproteomic annotations using ProtMapper to normalize site information from databases and text mining bioRxiv, 2022.

Preprint Vasilevsky NA, Matentzoglu NA, ..., Hoyt CT, ..., Mungall CJ, Hamosh A, Haendel MA MONDO: Unifying diseases for the world, by the world medRxiv, 2022.

Preprint Hoyt CT, Berrendorf M, Gaklin M, Tresp V, Gyori BM. A Unified Framework for Rank-based Evaluation Metrics for Link Prediction in Knowledge Graphs arXiv, 2022.

Preprint Gaklin M, Berrendorf M, Hoyt CT An Open Challenge for Inductive Link Prediction on Knowledge Graphs arXiv, 2022.

Preprint Niarakis A, Ostaszewski M, Mazein A, ..., Gyori BM, ..., Reinhard Schneider, the COVID-19 Disease Map Community A versatile and interoperable computational framework for the analysis and modeling of COVID-19 disease mechanisms bioRxiv, 2022.


Published Gyori BM, Bachman JA. From knowledge to models: automated modeling in systems and synthetic biology. Current Opinion in Systems Biology, 2021.

Published Ietswaart R, Gyori BM, Bachman JA, Sorger PK, Churchman S GeneWalk identifies relevant gene functions for a biological context using network representation learning Genome Biology, 2021 22(51).

Published Gyori BM, Bachman JA, Kolusheva D. A self-updating causal model of COVID-19 mechanisms built from the scientific literature Proceedings of the BioCreative VII Challenge Evaluation Workshop, 2021.

Published Wong J, Franz M, Siper MC, Fong D, Durupinar F, Dallago C, Luna A, Giorgi JM, Rodchenkov I, Babur O, Bachman JA, Gyori BM, Demir E, Bader G, Sander C. Author-sourced capture of pathway knowledge in computable form using Biofactoid eLife, 2021 10:e68292.

Published Ostaszewski M, Niarakis A, ... Gyori BM, Bachman JA, ..., Baling R, Schneider R. COVID-19 Disease Map, a computational knowledge repository of SARS-CoV-2 virus-host interaction mechanisms Molecular Systems Biology, 2021.

Preprint Moret N, Liu C, Gyori BM, Bachman JA, Steppi A, Taujale R, Huang LC, Hug C, Berginski M, Gomez S, Kannan N, Sorger PK. Exploring the understudied human kinome for research and therapeutic opportunities bioRxiv, 2021.


Published Nam KM, Gyori BM, Amethyst SV, Bates DJ, Gunawardena J Robustness and parameter geography in post-translational modification systems PLoS Computational Biology, 2020.

Published Steppi A, Gyori BM, Bachman JA. Adeft: Acromine-based Disambiguation of Entities from Text with applications to the biomedical literature Journal of Open Source Software, 2020.


Published Todorov PV, Gyori BM, Bachman JA, Sorger PK. INDRA-IPM: interactive pathway modeling using natural language with automated assembly. Bioinformatics, 2019.

Published Sharp R, Pyarelal A, Gyori BM, Alcock K, Laparra Egoitz, Valenzuela-Escárcega MA, Nagesh A, Yadav V, Bachman JA, Tang Z, Lent H, Luo F, Paul M, Bethard S, Barnard K, Morrison C, Surdeanu M. Eidos, INDRA, & Delphi: From Free Text to Executable Causal Models NAACL, 2019.

Published Hoyt C, Domingo-Fernández D, Aldisi R, Xu L, Kolpeja K, Spalek S, Wollert E, Bachman J, Gyori BM, Greene P, Hofmann-Apitius M. Re-curation and rational enrichment of knowledge graphs in Biological Expression Language Database, 2019.


Published Bachman JA, Gyori BM, Sorger PK. FamPlex: a resource for entity recognition and relationship resolution of human protein families and complexes in biomedical text mining. BMC Bioinformatics, 2018 19(1):248.
Repository: FamPlex


Announcements about the RAPTER project by LANL and by PNNL.

Award announcement : Charlie Hoyt awarded International Society for Biocuration, Excellence in Biocuration Early Career Award.

Award announcement : Ben Gyori awarded DARPA Director's Fellowship for 2022-23.

Award announcement : Grant supporting the MIRA project on the DARPA ASKEM program for 2022-26.

AAAS EurekAlert: Our application of the EMMAA system to neurofibromatosis, "Self-updating causal models to accelerate discovery in NF", won an award from the Children's Tumor Foundation. See also our presentation and report.

WIRED : Our research on human-machine collaboration was featured in WIRED UK, in the article " The merging of humans and machines is happening now", written by then director of DARPA, Arati Prabhakar.

The Guardian : Ben Gyori and John Bachman were interviewed by The Guardian in the tech podcast " Siri of the Cell". Here we introduce our approach to human-machine communication and the assembly of models from the scientific literature.

Harvard Medicine Magazine : Ben Gyori and John Bachman were interviewed for an article in Harvard Medicine Magazine. In "A Closer Read" (see section WALL-E), they talk about natural language processing and the INDRA system.

Harvard Medicine News : Ben Gyori was interviewed by the Harvard News to talk about how AI's Next Wave can be applied to scientific discovery in biology.

DARPA News & Events Our work on the DARPA ASKE program is described in this article.

Harvard Medicine News Our collaboration with the Churchman lab to develop GeneWalk was covered in the article "Panning for Genetic Gold".



Benjamin Gyori
Benjamin M. Gyori, PhD

Associate professor

Charles Tapley Hoyt
Charles Tapley Hoyt, PhD

Senior scientist

Sangeetha Vempati
Sangeetha Vempati

Research assistant

Klas Karis
Klas Karis

Scientific software developer

Tenzin Nanglo
Tenzin Nanglo

Scientific software developer

Past members and contributors

  • John Bachman, PhD - co-team lead (2015-2021)
  • Samuel Bunga - bioinformatics software developer (2020-2022)
  • Diana Kolusheva - scientific software developer (2018-2022)
  • Albert Steppi, PhD - scientific software developer (2018-2021)
  • Patrick Greene - scientific software developer (2017-2021)
  • Petar Todorov - scientific software developer (2016-2018)
  • Isabel Latorre, PhD - program manager (2016-2018)
  • P.S. Thiagarajan, PhD - visiting professor (2015-2018)
  • Daniel Milstein - scientific software developer (2018)
  • Robert Sheehan, PhD - postdoctoral fellow (2016-2018)
  • Kartik Subramanian, PhD - postdoctoral fellow (2015-2019)
  • Lily Chylek, PhD - postdoctoral fellow (2015-2021)
  • William Chen, PhD - project lead (2014-2015)
  • Bihan Dasgupta - summer intern (2023)
  • Andras Stirling - summer intern (2021)
  • Askar Kolushev - summer intern (2020)
  • Aditya Parmar - summer intern (2016)