=Paper=
{{Paper
|id=Vol-1747/BT103_ICBO2016
|storemode=property
|title=Collaborative Workspaces for Pathway Curation
|pdfUrl=https://ceur-ws.org/Vol-1747/BT103_ICBO2016.pdf
|volume=Vol-1747
|authors=Funda Durupinar-Babur,Metin Can Siper,Ugur Dogrusoz,Istemi Bahceci,Ozgun Babur,Emek Demir
|dblpUrl=https://dblp.org/rec/conf/icbo/Durupinar-Babur16
}}
==Collaborative Workspaces for Pathway Curation ==
https://ceur-ws.org/Vol-1747/BT103_ICBO2016.pdf
Collaborative Workspaces for Pathway Curation
Funda Durupinar-Babur1, Metin Can Siper2, Ugur Dogrusoz2, Istemi Bahceci2, Ozgun Babur1, Emek Demir1.
1 Oregon Health and Science University, Computational Biology Program, Portland, OR
2. Bilkent University, Dept. of Comp Engineering, Ankara, Turkey
Abstract— We present a web based visual biocuration require, potentially, a social change in the way we publish and
workspace, focusing on curating detailed mechanistic pathways. disseminate our results. There are already multiple efforts in
It was designed as a flexible platform where multiple humans, these directions – but they are currently very disjoint. In order
NLP and AI agents can collaborate in real-time on a common to enable exploring these directions, we developed a web-
model using an event driven API. We will use this platform for
based collaborative workspace as a common platform. We
exploring disruptive technologies that can scale up biocuration
such as NLP, human-computer collaboration, crowd-sourcing, also describe a pilot study, as an example downstream
alternative publishing and gamification. As a first step, we are application, where authors directly curate formal pathway
designing a pilot to include an author-curation step into the snippets that they discovered as a part of the publication
scientific publishing, where the authors of an article create process.
formal pathway fragments representing their discovery- heavily
assisted by computer agents. We envision that this “micro- II. A COLLABORATIVE WORKSPACE
curation” use-case will create an excellent opportunity to The platform is composed of a web-based graphical editor and
integrate multiple NLP approaches and semi-automated
an application server. Figure 1 shows the platform
curation.
architecture. The graphical editor is an extension of the
Keywords—biocuration, pathways, SBGNViz framework [1], which is a web application based on
Cytoscape.js [4] to visualize BioPAX [2] models represented
by SBGN Process Description Notation (SBGN-PD) [3].
I. INTRODUCTION Cytoscape.js provides a rich set of graph visualization and
Molecular biology studies the molecular components and manipulation features. SBGNViz adds domain specific glyphs,
mechanisms that control a cell’s response to stimuli. layouts and tool. Finally, our platform adds collaboration,
Traditionally, this was a piecemeal effort primarily conducted conflict resolution and a strong link to existing NLP tools.
through carefully-designed series of experiments that isolate,
elucidate and confirm a part of the mechanism under a certain The application server is based on Node.js – a server-side
context. A byproduct of this process is knowledge Javascript environment with an event-driven, asynchronous IO
fragmentation – putting these components into a mechanism, model. This non-blocking structure allows combining agents
often called a pathway, that can describe cellular behavior is that operate in different time scales. For example, extracting a
extremely challenging. One needs to assemble these pieces relevant piece of information from the literature might take
across many publications, negotiate differences due to minutes whereas a visualization action can be accomplished in
biological context and experimental setup, resolve conflicts seconds. The server keeps track of the graphical editor’s state
and create a coherent model. The majority of this knowledge
and updates an underlying shared JSON model. The model
integration happens informally through review articles within a
can be edited concurrently via an Operational Transformation
very limited scope – often focusing around a couple of proteins
or genes. (OT) library called Share.js. OT provides versioning,
concurrency control, conflict resolution in a manner similar to
The biology, however, is changing rapidly primarily due to modern distributed code versioning systems and is
system scale “-omic” profiling and “big data”. This, in turn, successfully used in large collaborative editing applications.
creates an urgent necessity for large scale, formal models of All operations are made persistent in a MongoDB database.
cellular processes – way beyond the scale of a current review
article. This led to a rapid proliferation of pathway databases or
curation groups- which currently stands at 600. Together, they
have curated hundreds of thousands detailed biochemical
reactions and millions of interactions. Yet, this is still only a
small amount of the knowledge in the literature (our estimation
is somewhere between 1 to 3%) and due to the rapid increase
in our knowledge, this gap widens every day. Curation efforts
– although extremely valuable-- also tend to be expensive. NIH
spent 1.2 billion dollars on supporting data curation in the last
decade– and the need is ever increasing.
How can we scale biocuration up by two orders of magnitude?
We believe that the solution lies in the intersection of NLP,
Artificial Intelligence and crowd-sourcing. It might also
linked to an existing pathway corpus. This model fragment will
be published as a supplement to the paper and can then be
harnessed easily by curators and algorithms to assemble
increasingly large mechanistic models. We envision that such
a “micro-curation” step, if successfully embedded to the
publication process, can enable rapid and scalable capture of
atomic facts about cellular processes. By getting the fragments
directly from the scientists who discovered them we hope to
reduce communication noise, ambiguity and interpretation
errors inherent to our current scientific communication and
curation process.
IV. A GENERAL TOOL FOR BIOCURATION
In parallel to this pilot study, we also plan to actively use this
platform to select most successful NLP and AI tools for
pathway curation. The open real-time nature of the platform
makes it extremely straightforward to swap, compare and
combine different NLP approaches. This platform can also be
used to extract non-interaction/pathway related information
such as diseases, drug targets and mutations and combine these
with pathway models. We can also explore how to extract
supporting information such as experimental evidence or
biological context. The web-based system enables
collaborative use-cases and it can be used to collect large
human curation data that can be used to build a gold standard
Figure 1. Platform architecture corpus. We believe that the platform can become a major tool
for biocuration and NLP communities.
Computer agents connect to the server through an API that ACKNOWLEDGMENT
uses socket.io interface. The API provides a two-way
communication channel. Therefore, agents can both make This work was funded by the DARPA Big Mechanism
changes to the model and get notifications about model program under ARO contract W911NF-14-1-0395.
updates. The server also provides a chat interface through
which human users and computer agents can send text REFERENCES
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