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<!DOCTYPE HTML>
<!--
Read Only by HTML5 UP
html5up.net | @ajlkn
Free for personal and commercial use under the CCA 3.0 license (html5up.net/license)
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<html lang="en">
<head>
<title>Gyori Lab for Computational Biomedicine</title>
<meta charset="utf-8"/>
<meta name="viewport" content="width=device-width, initial-scale=1, user-scalable=no"/>
<link rel="stylesheet" href="assets/css/main.css"/>
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<body class="is-preload">
<!-- Header -->
<section id="header">
<header>
<span class="image avatar"><img src="images/indra_logo.png" alt=""/></span>
<h1 id="logo"><a href="#">Gyori Lab for Computational Biomedicine</a></h1>
<!---<p>description/p> -->
</header>
<nav id="nav">
<ul style="margin: 0 0 100px 0">
<li><a href="#intro">Introduction</a></li>
<li><a href="#apps">Applications</a></li>
<li><a href="#projects" class="active">Projects and Funding</a></li>
<li><a href="#positions">Open Positions</a></li>
<li><a href="#pubs">Publications</a></li>
<li><a href="#media">Media</a></li>
<li><a href="#team">Team</a></li>
<li><a href="#collaborators">Collaborators</a></li>
</ul>
<h1>Tweets from @IndraSysBio</h1>
<div class="container">
<a class="twitter-timeline"
data-height="250"
data-chrome="noheader,nofooter"
data-dnt="true"
href="https://twitter.com/IndraSysBio?ref_src=twsrc%5Etfw"
>Tweets from @IndraSysBio</a>
<script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>
</div>
</nav>
<footer>
<ul class="icons">
<li><a href="https://twitter.com/indrasysbio" class="icon fa-twitter"><span class="label">Twitter</span></a>
</li>
<li><a href="https://github.com/gyorilab" class="icon fa-github"><span class="label">GitHub</span></a></li>
<li><a href="mailto:[email protected]" class="icon fa-envelope"><span class="label">Email</span></a>
</li>
</ul>
</footer>
</section>
<!-- Wrapper -->
<div id="wrapper">
<!-- Main -->
<div id="main">
<div class="container" style="padding-top: 25px;">
<section>
<p> This website describes projects that are led by Benjamin M. Gyori
at <a href="https://www.northeastern.edu/">Northeastern University</a> since August 2023.
Previously, the research group was
based at the
<a href="https://labsyspharm.org/">Laboratory of Systems Pharmacology</a>,
<a href="http://hits.harvard.edu/">Harvard Program in Therapeutic Science (HiTS)</a>,
at <a href="https://hms.harvard.edu/">Harvard Medical School</a>.
</p>
</section>
</div>
<!-- Two -->
<section id="intro">
<div class="container">
<h3>Introduction</h3>
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.
</div>
</section>
<section id="apps">
<div class="container">
<h3>Applications</h3>
<div class="features">
<article>
<a href="http://github.com/sorgerlab/indra" class="image"><img src="images/indra_pipeline.gif"
alt="INDRA"/></a>
<div class="inner">
<h4>INDRA</h4>
<p>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.
<br/>
<a href="https://github.com/sorgerlab/indra">Code</a>
<a href="https://indra.readthedocs.io">Docs</a>
<a href="http://api.indra.bio:8000">REST API</a></p>
</div>
</article>
<article>
<a href="https://emmaa.indra.bio" class="image"><img src="images/emmaa.png" alt="EMMAA"/></a>
<div class="inner">
<h4>EMMAA</h4>
<p>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.
<br/>
<a href="https://emmaa.indra.bio">Website</a>
<a href="https://github.com/gyorilab/emmaa">Code</a>
<a href="https://emmaa.readthedocs.io">Docs</a>
<a href="https://emmaa.indra.bio/doc">REST API</a>
</div>
</article>
<article>
<a href="https://discovery.indra.bio" class="image"><img src="images/discovery.png" alt="INDRA Biomedical Discovery Engine"/></a>
<div class="inner">
<h4>INDRA Biomedical Discovery Engine</h4>
<p>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.
<br/>
<a href="https://discovery.indra.bio">Website</a>
<a href="https://github.com/bgyori/indra_cogex">Code</a>
</div>
</article>
<article>
<a href="https://db.indra.bio/" class="image"><img src="images/indra_db.png" alt=""/></a>
<div class="inner">
<h4>INDRA Database</h4>
<p>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.
<br/>
<a href="https://db.indra.bio">Website</a>
<a href="https://github.com/gyorilab/indra_db/blob/master/indra_db_service/README.md">
REST API</a>
<a href="https://github.com/gyorilab/indra_db">Code</a>
</p>
</div>
</article>
<article>
<a href="http://pathwaymap.indra.bio/" class="image"><img src="images/indra_ipm.png"
alt=""/></a>
<div class="inner">
<h4>INDRA-IPM (Interactive Pathway Map)</h4>
<p>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.
<br/>
<a href="http://pathwaymap.indra.bio">Website</a>
<a href="https://github.com/sorgerlab/indra_pathway_map">Code</a>
</p>
</div>
</article>
<article>
<a href="http://dialogue.bio" class="image"><img src="images/bob_sbgn.png" alt=""/></a>
<div class="inner">
<h4>Dialogue.bio</h4>
<p>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.
<br/><a href="http://dialogue.bio">Website</a>
</p>
</div>
</article>
<article>
<a href="#" class="image"><img src="images/clare.png" alt=""/></a>
<div class="inner">
<h4>CLARE machine assistant</h4>
<p>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.
<br/>
<a href="https://www.youtube.com/watch?v=hZRY6G2cwEU">Demo video</a>
</div>
</article>
<article>
<a href="http://dsmt-e.com" class="image"><img src="images/wm_causemos.png" alt=""/></a>
<div class="inner">
<h4>INDRA World</h4>
<p>
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 were also part of the <a href="https://web.archive.org/web/20230329202650/https://www.dsmt-e.com/">DSMT-E project</a>,
a large collaboration funded by DARPA and the Gates Foundation to develop
AI-driven decision support tools for Ethiopia.
<br/>
<a href="https://github.com/gyorilab/indra_world">Code</a>
<a href="https://worldmodelers.com/">World Modelers technical website</a>
</p>
</div>
</article>
<article>
<a href="http://depmap.indra.bio" class="image"><img src="images/depmap.png" alt=""/></a>
<div class="inner">
<h4>Network search and DepMap explainer</h4>
<p>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 <a href="http://depmap.org">depmap.org</a>.
<br/>
<a href="http://depmap.indra.bio">DepMap explainer</a>
<a href="https://network.indra.bio">Network search</a>
</p>
</div>
</article>
<article>
<a href="https://covid19.indra.bio" class="image"><img src="images/covid19_image.png"
alt=""/></a>
<div class="inner">
<h4>Application to COVID-19</h4>
<p>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.
<br/>
<a href="https://covid19.indra.bio/">Website</a>
</p>
</div>
</article>
<article>
<a href="https://panacea.indra.bio" class="image"><img src="images/ion_channel_kb_network.png"
alt=""/></a>
<div class="inner">
<h4>Application to studying pain and inflammation</h4>
<p>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.
<br/>
<a href="https://panacea.indra.bio/">Website</a>
</p>
</div>
</article>
<article>
<a href="https://github.com/gyorilab/adeft" class="image"><img src="images/adeft.png"
alt=""/></a>
<div class="inner">
<h4>Adeft</h4>
<p>
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.
<br/>
<a href="https://github.com/gyorilab/adeft">Code</a>
</p>
</div>
</article>
<article>
<a href="http://grounding.indra.bio" class="image"><img src="images/gilda.png" alt=""/></a>
<div class="inner">
<h4>Gilda</h4>
<p>
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.
<br/>
<a href="http://grounding.indra.bio/">Website and REST API</a>
<a href="https://github.com/gyorilab/gilda">Code</a>
</p>
</div>
</article>
<article>
<a href="https://biopragmatics.github.io" class="image">
<img src="images/biopragmatics.png" alt="Biopragmatics tools"/>
</a>
<div class="inner">
<h4>Biopragmatics stack</h4>
<p>
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).
<br/>
<a href="https://biopragmatics.github.io/">Website</a>
<a href="https://github.com/biopragmatics/">Code</a>
</p>
</div>
</article>
</div>
</div>
</section>
<!-- One -->
<section id="projects">
<!--
<div class="image main" data-position="center">
<img src="images/banner.jpg" alt="" />
</div>
<header class="major">
<h2>INDRA labs</h2>
<p>abcdefg</p>
</header>
-->
<div class="container">
<h3>Projects and Funding</h3>
<h4>Current</h4>
<div id="askem">
<p>
<strong>Automating Scientific Knowledge Extraction and Modeling (ASKEM)</strong>
The <a href="https://www.darpa.mil/news-events/2021-12-06" target="_blank">DARPA ASKEM program</a> 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 <a href="https://github.com/gyorilab/mira">MIRA system</a>
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.
<br/>
<i>
Funded by the Defense Advanced Research Projects Agency
under award HR00112220036. (2022-)
</i>
</p>
</div>
<div id="rapter">
<p>
<strong>Rapid Assessment of Platform Technologies to Expedite Response (RAPTER)</strong>
The <a href="https://discover.lanl.gov/news/1027-vaccine-development/" target="_blank">RAPTER program</a>
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.
<br/>
<i>
Funded by the Defense Threat Reduction Agency. (2023-)
</i>
</p>
</div>
<div id="czi-bioregistry">
<p>
<strong>Advancing Data Integration and Discovery in Biomedicine with the Bioregistry</strong>
This project aims to support the development, maintenance, and outreach efforts of the
<a href="https://bioregistry.io/">Bioregistry</a>, a tool designed to facilitate data integration and standardization in biomedical
research. We are developing a semi-automated curation framework to keep pace with the rapid
introduction of new semantic spaces, harmonizing inconsistent registries, and developing new
ways to access the Bioregistry through different formats, programming languages, and user
interfaces. In parallel, we are expanding our training and outreach efforts to engage academic
and industry groups, major academic publishers, and funding agencies to promote wider adoption
of Bioregistry.
<br/>
<i>
Funded by the Chan Zuckerberg Initiative (CZI) under award 2023-329850.
(2023-)
</i>
</p>
</div>
<h4>Past</h4>
<div id="big-mechanism">
<p><strong>Big Mechanism</strong> 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.
<br/>
<i>Funded by the Defense Advanced Research Projects Agency
under award W911NF-14-1-0397 (2014-2019).</i>
</p>
</div>
<div id="cwc">
<p><strong>Communicating with Computers</strong> 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.
<br/>
<i>Funded by the Defense Advanced Research Projects Agency
under award W911NF-15-1-0544 (2015-2021).</i>
</p>
</div>
<div id="asdf">
<p><strong>Automated Scientific Discovery Framework</strong>
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.
<br/>
<i>Funded by the Defense Advanced Research Projects Agency
under award W911NF018-1-0124. (2018-2020)</i>
</p>
</div>
<div id="world-modelers">
<p><strong>World Modelers</strong> 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.
<br/>
<i>Funded by the Defense Advanced Research Projects Agency
under award W911NF-18-1-0014. (2017-2022)</i>
</p>
</div>
<div id="aske">
<p><strong>Automating Scientific Knowledge Extraction</strong>
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. <br/>
<i>Funded by the Defense Advanced Research Projects Agency
under award HR00111990009. (2018-2021)</i>
</p>
</div>
<div id="panacea">
<p><strong>Panacea</strong>
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.<br/>
<i>Funded by the Defense Advanced Research Projects Agency
under award HR00111920022. (2019-2023)</i>
</p>
</div>
<div id="yfa">
<p><strong>DARPA Young Faculty Award and Director's Fellowship Award</strong>
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.
<br/>
<i>Funded by the Defense Advanced Research Projects Agency
under award W911NF2010255. (2020-2023)</i>
</p>
</div>
</div>
</section>
<section id="positions">
<div class="container">
<h3>Open Positions</h3>
We are looking for new lab members in multiple different positions:
<dl>
<dt>PhD Candidate</dt>
<dd>Apply to the Computer Science PhD program at
<a href="https://www.khoury.northeastern.edu/apply/phd-apply/">https://www.khoury.northeastern.edu/apply/phd-apply/</a> or the
Bioengineering PhD program at <a href="https://bioe.northeastern.edu/academics/graduate-studies/phd-bion/">https://bioe.northeastern.edu/academics/graduate-studies/phd-bion/</a>.
</dd>
<dt>Scientific software developer</dt>
<dd>Apply at <a href="https://jobs.chronicle.com/job/37567504/sr-systems-software-developer">https://jobs.chronicle.com/job/37567504/sr-systems-software-developer</a>.</dd>
<dt>Postdoctoral Fellow</dt>
<dd>For research-oriented postdoctoral roles, please reach out directly.</dd>
<dt>Biocurator Positions</dt>
<dd>We are looking for a part-time or volunteer biocurator for the <a href="https://bioregistry.io">Bioregistry</a> project. See details <a href="https://bit.ly/bioregistry-curator-2024">here</a>.</dd>
</dl>
</div>
</section>
<!-- Four -->
<section id="pubs">
<div class="container">
<h3>Publications</h3>
Note that these publications are ones specific to the team's
projects. Gyori's full publication list is available
on <a href="https://scholar.google.com/citations?user=kH9LOHMAAAAJ&hl=en" target="_blank">
Google Scholar</a>.
<p></p>
<h4>2024</h4>
<p>
<span class="badge badge-published">Published</span>
Sara Mohammad-Taheri, Pruthvi Prakash Navada, <strong>Charles Tapley Hoyt</strong>, Jeremy Zucker, Karen Sachs, <strong>Benjamin M. Gyori</strong> and Olga Vitek.
<a href="https://doi.org/10.1093/bioinformatics/btae527">Eliater: a Python package for estimating outcomes of
perturbations in biomolecular networks.</a>
<i>Bioinformatics</i> 2024.
</p>
<p>
<span class="badge badge-published">Published</span>
Benjamin M. Gyori and Olga Vitek
<a href="https://www.nature.com/articles/s41592-024-02324-4">
Beyond protein lists: AI-assisted interpretation of proteomic investigations in the context of evolving scientific knowledge
</a>
<i>Nature Methods</i> 2024.
</p>
<p>
<span class="badge badge-published">Published</span>
Charles Tapley Hoyt and Benjamin M. Gyori
<a href="https://www.nature.com/articles/s41597-024-03406-w">
The O3 guidelines: open data, open code, and open infrastructure for sustainable curated scientific resources
</a>
<i>Scientific Data</i> 2024.
<br/>Summary video: <a href="https://www.youtube.com/watch?v=kuJsl-rRjZY">https://youtu.be/kuJsl-rRjZY</a></p>
</p>
<p>
<span class="badge badge-published">Published</span>
Jain A, <strong>Gyori BM</strong>, Hakim S, Jain A, Sun L,
Petrova V, ..., <strong>Bunga S</strong>, ..., PK Sorger, CJ Woolf
<a href="https://doi.org/10.1038/s41590-024-01857-2">Nociceptor-immune interactomes reveal insult-specific immune signatures of pain</a>.
<i>Nature Immunology</i>, 2024
</p>
<p>
<span class="badge badge-published">Published</span>
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, <strong>Charles Tapley Hoyt</strong>,
..., Lawrence E. Hunter
<a href="https://www.nature.com/articles/s41597-024-03171-w">An open source knowledge graph ecosystem for the life sciences</a>.
<i>Scientific Data</i>, 2024
</p>
<p>
<span class="badge badge-preprint">Preprint</span>
Lindsey N Anderson, <strong>Charles Tapley Hoyt</strong>, Jeremy Zucker, Jeremy R. Teuton, Andrew D. McNaughton, <strong>Klas Klaris</strong>, Natasha N. Arokium-Christian, Jackson T. Warley, Zachary R. Stromberg, <strong>Benjamin M. Gyori</strong>, and Neeraj Kumar
<a href="https://osf.io/preprints/osf/mtx9b">Computational Tools and Data Integration to Accelerate Vaccine Development: Challenges, Opportunities, and Future Directions</a>.
<i>OSF preprints</i>, 2024
</p>
<h4>2023</h4>
<p>
<span class="badge badge-published">Published</span>
Garano, M. A., ..., <strong>Gyori, B. M.</strong>, ..., <strong>Hoyt, C. T.</strong>,
..., Robinson, P. N.
<a href="https://doi.org/10.1093/nar/gkad1005">
The Human Phenotype Ontology in 2024: phenotypes around the world
</a>
<i>Nucleic Acids Research</i>, gkad1005, 2023.
</p>
<p>
<span class="badge badge-published">Published</span>
Bachman JA, Gyori BM, Sorger PK
<a href="https://doi.org/10.15252/msb.202211325">
Automated assembly of molecular mechanisms at scale from text mining and curated databases
</a>
<i>Molecular Systems Biology</i>, e11325, 2023.
</p>
<p>
<span class="badge badge-published">Published</span>
<strong>Hoyt, C. T.</strong>, Hoyt, A. L., and <strong>Gyori, B. M.</strong>
<a href="https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btad130/7077133">
Prediction and Curation of Missing Biomedical Identifier Mappings with Biomappings
</a>
<i>Bioinformatics</i>, 39(4), btad130, 2023.
</p>
<p>
<span class="badge badge-published">Published</span>
Pillich RT, Chen J, Churas C, Fong D, <strong>Gyori BM</strong>, Ideker T,
<strong>Karis K</strong>, Liu SN, Ono K, Pico A, Pratt D
<a href="https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btad118/7070501">
NDEx IQuery: a multi-method network gene set analysis leveraging the Network Data Exchange
</a>
<i>Bioinformatics</i>, 39(3), btad118, 2023.
</p>
<p>
<span class="badge badge-published">Published</span>
Lobentanzer S, Aloy P, Baumbach J, Bohar B, Danhauser K,
Doğan T, Dreo J, Dunham I, Fernandez-Torras A, <strong>Gyori BM</strong>,
Hartung M, <strong>Hoyt CT</strong>, 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
<a href="https://doi.org/10.1038/s41587-023-01848-y">
Democratising Knowledge Representation with BioCypher
</a>
<i>Nature Biotechnology</i>, 2023.
</p>
<p>
<span class="badge badge-published">Published</span>
Niarakis A, Ostaszewski M, Mazein A, ..., <strong>Gyori BM</strong>,
..., Reinhard Schneider
<a href="https://www.frontiersin.org/articles/10.3389/fimmu.2023.1282859/abstract">
Drug-Target identification in COVID-19 disease mechanisms using computational systems biology approaches
</a>
<i>Frontiers in Immunology</i>, 2023.
</p>
<h4>2022</h4>
<p>
<span class="badge badge-published">Published</span>
<strong>Hoyt CT</strong>, Balk M, Callahan, TJ, Domingo-Fernandez D, Haendel MA, Hegde HB,
Himmelstein DS, <strong>Karis K</strong>, Kunze J, Lubiana T, Matentzoglu N, McMurry J, Moxon S,
Mungall CJ, Rutz A, Unni DR, Willighagen E, Winston D, & <strong>Gyori BM</strong>
<a href="https://doi.org/10.1038/s41597-022-01807-3">
Unifying the Identification of Biomedical Entities with the Bioregistry
</a>
<i>Scientific Data</i>, 2022.
</p>
<p>
<span class="badge badge-published">Published</span>
Gyori BM, Hoyt CT, Steppi A,
<a href='https://doi.org/10.1093/bioadv/vbac034'>
Gilda: biomedical entity text normalization with machine-learned disambiguation as a
service
</a>
<i>Bioinformatics Advances</i>, 2022.
</p>
<p>
<span class="badge badge-published">Published</span>
Scholten B, Guerrero Simón L, Krishnan S,
Vermeulen R, Pronk A, <strong>Gyori BM</strong>,
<strong>Bachman JA</strong>, Vlaanderen J, Stierum R.
<a href="https://ehp.niehs.nih.gov/doi/full/10.1289/EHP9112">
Automated Network Assembly of Mechanistic Literature for
Informed Evidence Identification to Support Cancer Risk Assessment
</a>
<i>Environmental Health Perspectives</i>, 2022.
</p>
<p>
<span class="badge badge-published">Published</span>
Gyori BM and Hoyt CT.
<a href='https://joss.theoj.org/papers/10.21105/joss.04136'>
PyBioPAX: biological pathway exchange in Python
</a>
<i>Journal of Open Source Software</i>, 2022.
</p>
<p>
<span class="badge badge-published">Published</span>
Balabin H, <strong>Hoyt CT</strong>, Birkenbihl C,
<strong>Gyori BM</strong>, <strong>Bachman JA</strong>,
Kodamullil AT, Ploeger PG, Hofmann-Apitius M, Domingo-Fernandez D.
<a href='https://doi.org/10.1093/bioinformatics/btac001'>
STonKGs: A Sophisticated Transformer Trained on Biomedical Text and Knowledge Graphs
</a>
<i>Bioinformatics</i>, 2022.
</p>
<p>
<span class="badge badge-published">Published</span>
Balabin H, <strong>Hoyt CT</strong>,
<strong>Gyori BM</strong>, <strong>Bachman JA</strong>,
Kodamullil AT,Hofmann-Apitius M, Domingo-Fernandez D.
<a href='http://ceur-ws.org/Vol-3127/paper-13.pdf'>
ProtSTonKGs: A Sophisticated Transformer Trained on Protein Sequences, Text, and Knowledge Graphs
</a>
<i>SWAT4HCLS</i>, 2022.
</p>
<p>
<span class="badge badge-published">Published</span>
N Matentzoglu, JP Balhoff, SM Bello, C Bizon, M Brush, TJ
Callahan, CG Chute, WD Duncan, CT Evelo, D Gabriel, J Graybeal, A Gray, <strong>BM Gyori</strong>, M
Haendel, H Harmse, NL Harris, I Harrow, H Hegde, AL Hoyt, <strong>CT Hoyt</strong>, ..., CJ Mungall
<a href='https://doi.org/10.1093/database/baac035'>
A Simple Standard for Sharing Ontological Mappings (SSSOM)
</a>
<i>Database</i>, 2022.
</p>
<p>
<span class="badge badge-published">Published</span>
Bonner S, Barrett IP, Ye C, Swiers R,
Engkvist O, <strong>Hoyt CT</strong>, Hamilton WL
<a href="https://doi.org/10.1016/j.ailsci.2022.100036">
Understanding the Performance of Knowledge Graph Embeddings in Drug Discovery
</a>
<i>Artificial Intelligence in the Life Sciences</i>, 2022.
</p>
<p>
<span class="badge badge-published">Published</span>
Doherty LM, Mills CE, Boswell SA, Liu X, <strong>Hoyt CT, Gyori
BM</strong>,
Buhrlage SJ, Sorger PK.
<a href='https://doi.org/10.7554/eLife.72879'>
Integrating multi-omics data reveals function and therapeutic potential of deubiquitinating
enzymes
</a>
<i>eLife</i> 11:e72879, 2022.
<br/>Website: <a href="https://labsyspharm.github.io/dubportal/">DUB portal</a>
</p>
<p>
<span class="badge badge-published">Published</span> Mohammad-Taheri S,
Zucker J,
<strong>Hoyt CT</strong>,
Sachs K,
Tewari V,
Ness R,
Vitek O
<a href="https://doi.org/10.1093/bioinformatics/btac251">
Do-calculus enables causal reasoning with latent variable models
</a>
<i>Bioinformatics</i>, 2022.
</p>
<p>
<span class="badge badge-published">Published</span>
de Crécy-lagard V, de Hegedus RA, Arighi C, ..., <strong>Gyori BM</strong>, ..., Xu J
<a href="https://doi.org/10.1093/database/baac062">
A roadmap for the functional annotation of protein families: a community perspective
</a>
<i>Database</i>, 2022.
</p>
<p>
<span class="badge badge-published">Published</span>
Rozemberczki B, <strong>Hoyt CT</strong>, Gogleva A, Grabowski P,
<strong>Karis K</strong>, Lamov A, Andriy N, Nilsson S,
Ughetto M, Wang Y, Derr T, <strong>Gyori BM</strong>.
<a href='https://doi.org/10.1145/3534678.3539023'>
ChemicalX: A Deep Learning Library for Drug Pair Scoring
</a>
<i>KDD</i>, 2022.
</p>
<p>
<span class="badge badge-published">Published</span>
Hungerford J, Chan YS, MacBride J,
<strong>Gyori BM</strong>, ...,
Reynolds M, Surdeanu M, Bethard S, Sharp R
<a href="https://aclanthology.org/2022.hcinlp-1.1/">
Taxonomy Builder: a Data-driven and User-centric Tool for Streamlining Taxonomy Construction
</a>
<i>NAACL HCI+NLP</i>, 2022.
</p>
<p>
<span class="badge badge-published">Published</span>
Bonner S, Barrett IP, Ye C, Swiers R, Engkvist O,
Bender A, <strong>Hoyt CT</strong>, Hamilton WL
<a href="https://doi.org/10.1093/bib/bbac404">
A Review of Biomedical Datasets Relating to Drug Discovery: A Knowledge Graph Perspective
</a>
<i>Briefings in Bioinformatics</i>, 2022.
</p>
<p>
<span class="badge badge-published">Published</span>
Matentzoglu N, Goutte-Gattat D, Tan SZK, Balhoff JP, Carbon S, Caron AR,
Duncan WD, Flack JE, Haendel M, Harris NL, Hogan WR, <strong>Hoyt CT</strong>,
Jackson RC, Kim H, Kir H, Larralde M, McMurry JA, Overton JA, Peters B, ... Osumi-Sutherland D.
<a href="https://doi.org/10.1093/database/baac087">
Ontology Development Kit: a toolkit for building, maintaining, and standardising biomedical ontologies
</a>
<i>Database</i>, 2022.
</p>
<p>
<span class="badge badge-preprint">Preprint</span>
Mohammad-Taheri S, Tewari V, Kapre R, Rahiminasab E, Sachs K,
<strong>Hoyt CT</strong>, Zucker J, Vitek O
<a href="https://arxiv.org/abs/2210.13423">
Experimental design for causal query estimation in partially observed biomolecular networks
</a>
<i>arXiv</i>, 2022.
</p>
<p>
<span class="badge badge-preprint">Preprint</span> Bachman JA, Sorger PK, Gyori BM.
<a href='https://doi.org/10.1101/822668'>
Assembling a corpus of phosphoproteomic annotations using ProtMapper to normalize site
information from databases and text mining
</a>
<i>bioRxiv</i>, 2022.
</p>
<p>
<span class="badge badge-preprint">Preprint</span>
Vasilevsky NA, Matentzoglu NA, ..., <strong>Hoyt
CT</strong>, ..., Mungall CJ, Hamosh A, Haendel MA
<a href="https://doi.org/10.1101/2022.04.13.22273750">
MONDO: Unifying diseases for the world, by the world
</a>
<i>medRxiv</i>, 2022.
</p>
<p>
<span class="badge badge-preprint">Preprint</span>
<strong>Hoyt CT</strong>, Berrendorf M, Gaklin M,
Tresp V, <strong>Gyori BM</strong>.
<a href='https://arxiv.org/abs/2203.07544'>
A Unified Framework for Rank-based Evaluation Metrics for
Link Prediction in Knowledge Graphs
</a>
<i>arXiv</i>, 2022.
</p>
<p>
<span class="badge badge-preprint">Preprint</span>
Gaklin M, Berrendorf M, <strong>Hoyt CT</strong>
<a href="https://arxiv.org/abs/2203.01520">
An Open Challenge for Inductive Link Prediction on Knowledge Graphs
</a>
<i>arXiv</i>, 2022.
</p>
<h4>2021</h4>
<p>
<span class="badge badge-published">Published</span>
Gyori BM, Bachman JA.
<a href="https://doi.org/10.1016/j.coisb.2021.100362">
From knowledge to models: automated modeling in systems and synthetic biology.
</a>
<i>Current Opinion in Systems Biology</i>, 2021.
</p>
<p>
<span class="badge badge-published">Published</span>
Ietswaart R, <strong>Gyori BM</strong>, <strong>Bachman JA</strong>, Sorger PK, Churchman S
<strong>
<a href='https://genomebiology.biomedcentral.com/articles/10.1186/s13059-021-02264-8'>
GeneWalk identifies relevant gene functions for a biological context using network
representation learning
</a>
</strong>
<i>Genome Biology</i>, 2021 22(51).
</p>
<p>
<span class="badge badge-published">Published</span>
<strong>Gyori BM</strong>, <strong>Bachman JA</strong>,
<strong>Kolusheva D</strong>.
<a href='https://biocreative.bioinformatics.udel.edu/media/store/files/2021/Track4_pos_5_BC7_submission_195-3.pdf'>
A self-updating causal model of COVID-19 mechanisms built from the scientific literature
</a>
<i>Proceedings of the BioCreative VII Challenge Evaluation Workshop</i>, 2021.
</p>
<p>
<span class="badge badge-published">Published</span>
Wong J, Franz M, Siper MC, Fong D, Durupinar F, Dallago C, Luna A, Giorgi JM,
Rodchenkov I, Babur O, <strong>Bachman JA</strong>, <strong>Gyori BM</strong>,
Demir E, Bader G, Sander C.
<a href='https://elifesciences.org/articles/68292'>
Author-sourced capture of pathway knowledge in computable form using Biofactoid
</a>
<i>eLife</i>, 2021 10:e68292.
</p>
<p>
<span class="badge badge-published">Published</span>
Ostaszewski M, Niarakis A, ... <strong>Gyori BM</strong>, <strong>Bachman JA</strong>,
..., Baling R, Schneider R.
<a href='https://doi.org/10.15252/msb.202110387'>
COVID-19 Disease Map, a computational knowledge repository of SARS-CoV-2 virus-host
interaction mechanisms
</a>
<i>Molecular Systems Biology</i>, 2021.
</p>
<p>
<span class="badge badge-preprint">Preprint</span>
Moret N, Liu C, <strong>Gyori BM, Bachman JA, Steppi A</strong>,
Taujale R, Huang LC, Hug C, Berginski M, Gomez S, Kannan N, Sorger PK.
<a href='https://doi.org/10.1101/2020.04.02.022277'>
Exploring the understudied human kinome for research and therapeutic opportunities
</a>
<i>bioRxiv</i>, 2021.
</p>
<h4>2020</h4>
<p>
<span class="badge badge-published">Published</span>
Nam KM, <strong>Gyori BM</strong>, Amethyst SV, Bates DJ, Gunawardena J
<a href='https://doi.org/10.1371/journal.pcbi.1007573'>
Robustness and parameter geography in post-translational modification systems
</a>
<i>PLoS Computational Biology</i>, 2020.
</p>
<p>
<span class="badge badge-published">Published</span>
<strong>Steppi A, Gyori BM, Bachman JA</strong>.
<a href='https://joss.theoj.org/papers/10.21105/joss.01708'>
Adeft: Acromine-based Disambiguation of Entities from Text with applications to the
biomedical literature
</a>
<i>Journal of Open Source Software</i>, 2020.
</p>
<h4>2017-2019</h4>
<p>
<span class="badge badge-published">Published</span>
Todorov PV, Gyori BM, Bachman JA, Sorger PK. <strong>
<a href="https://doi.org/10.1093/bioinformatics/btz289">INDRA-IPM: interactive pathway modeling