Presented at the Global Pharma R&D Informatics Congress. To find out more, visit: www.global-engage.com Text mining extracts complex information from text (entities, events and epistemic knowledge). It can be used to support pathway construction and the design of experiments by extracting evidence from literature. In this presentation, Sophia Ananiadou, Director of the National Centre for Text Mining, discusses bridging the gap between knowledge and text in cancer biology.

1. Machine Reading for Cancer
Biology
Sophia Ananiadou
National Centre for Text Mining
School of Computer Science
University of Manchester

2. End-to-end text mining system
• Machine reading for pathways
– Event extraction
– Uncertainty detection for ranking
• Integration of machine reading tools
– Argo and OpenMinTeD plaDorms
• Interactive visual analytics
– LitPathExplorer

3. Motivation
3
To support pathway construction and design of
experiments
• extract evidence from literature
• events, entities, contextual interpretation
For these, we need to
• understand pathway representations
• bridge the gap between knowledge and
text
• read against models (deep reading)

4. From concepts to events
1 Concept recognition
2 Interaction recognition
3 Concept and interaction identification
DrugBank:DB06712 DrugBank:DB00682 DrugBank:DB04610

5. 5
The Big Mechanism: reading, assembly, experiments
Courtesy: Paul Cohen
http://nactem.ac.uk/big_mechanism/

6. Tools for Event Extraction

7. EventMine
• EventMine: a machine learning pipeline event
extraction system
– Several parse results, dictionaries
– Coreference resolution, domain adaptation
7
http://www.nactem.ac.uk/EventMine/
Miwa, M. & Ananiadou, S. (2015) Adaptable, high recall, event extraction system with minimal
configuration, BMC Bioinformatics, 16(10), S7
Miwa, M., Thompson, P. and Ananiadou, S. (2012) Boosting automatic event extraction from
the literature using domain adaptation and coreference resolution. Bioinformatics, 28(13)

8. Linking interactions (events) to
pathways 1. The mitotic arrest-deficient
protein Mad1 forms a complex
with Mad2, which is required
for imposing mitotic arrest on
cells in which the spindle
assembly is perturbed. PMID:
18981471
2. Mad1, an upstream regulator of
Mad2, forms a tight core
complex with Mad2 and
facilitates Mad2 binding to
Cdc20. PMID: 18318601
8
2013 Beyond linking reactions
to documents at coarse
level

9. Event interpretation
Protein
MUC1
Theme 1
in RAS
Binding
binding to
Event trigger
Protein
PKM2
Theme 2
Event
argument
Entity
argument
Chemical
BRAF
Cause
is notResults suggest that
SIMPLE EVENT
COMPLEX EVENT
Theme
*Complex events have at
lest one argument that is
an event on its own
Regulation
required for
Event trigger

10. Textual Mentions in Context
• Our results prove that BRAF is required for MUC1 binding to PKM2
– Strong certainty
• Our results suggest that BRAF is required for MUC1 binding to PKM2
– Some hedging/speculation
• Our results indicate that BRAF may be required for MUC1 binding to PKM2
– Strong hedging/speculation
• There is scarce evidence that BRAF is required for MUC1 binding to PKM2
– Hedging
• We are going to test whether BRAF is required for MUC1 binding to PKM2
– Investigation
• Oeen BRAF is required for MUC1 binding to PKM2
– Frequency/time limitation
• Whether BRAF is required for MUC1 binding to PKM2 is out of the scope of
this work
– Admission of lack of knowledge

11. Uncertainty cues
11
BioNLP-ST, GENIA-MK

12. Hybrid approach
Dependency relations between cues and event triggers
Automated Rule Induction (from corpus)
1. EventMine (to identify event triggers)
2. Deep parsing (to identify dependencies)
3. Cue lists
Machine Learner (Random Forest)
1. Lexical (e.g. cues, POS tags, event-trigger surface form)
2. Syntactic (e.g. shortest path, dependency cue-trigger)
3. Semantic (e.g. event type, argument type/role)

13. Multiple mentions of the same event
• Our results prove that BRAF is
required for MUC1 binding to PKM2
• Our results suggest that BRAF is
required for MUC1 binding to PKM2
• Our results indicate that BRAF may
be required for MUC1 binding to
PKM2
• There is scarce evidence that BRAF is
required for MUC1 binding to PKM2
• We are going to test whether BRAF
is required for MUC1 binding to
PKM2
• Oeen BRAF is required for MUC1
binding to PKM2
• Whether BRAF is required for MUC1
binding to PKM2 is out of the scope
of this work
The same interaction can be
mentioned:
• In multiple sentences of the same
paper
• In multiple papers
• With different levels of certainty
in each mention
We need to consolidate different
uncertainty values from each
mention to one “confidence” score

14. Consolidation over several mentions
Ex
Ex Ex
Adapting subjective logic
framework:
Ex Ex
?
Each mention of Ex
mapped to a pathway is
considered as the
subjective opinion of the
author for the interaction
described by the Ex.
Ex ωx = (bx , dx, ux,α)
belief
disbelief
uncertainty
base rate
uncertainty
identification
prior
probabilities
negation
identification

15. Evaluation - pathway models
• B-cell acute lymphoblastic leukemia model
(Pathway studio)
– 72 interactions, 260 evidence passages manually
selected, 1-20 sentences per interaction
– 12% flagged uncertain by our system
Zerva, C., Batista-Navarro, R., Day, P. and S. Ananiadou (2017) Using uncertainty to link
and rank evidence from biomedical literature for model reconstruction, Bioinformatics

16. Results
• Leukemia Pathway (7 annotators) ~ Pathway
Studio
• Average accuracy on sentence level: 0.96
• Average accuracy on interaction level: 0.87
– 1-20 sentences per interaction

17. Event interpretation
• Uncertainty scoring as an expressive
confidence measure
• Hybrid framework
• Value for each event mentioned in a sentence
– Consolidated uncertainty values from different
papers
• Effort to decrease manual effort and select
more certain events
17

18. • Web-based, graphical TM workbench
• Unstructured Information Management Architecture (UIMA)
standard
• Rich library of TM components
• Allows Cloud and high-performance computing
• StraighDorward integration of TM analytics
– modular, extensible, reconfigurable,
reusable workflows
Source: LEGO DUPLO
Database: The Journal of Biological Databases and Curation (2014)
Text Mining-assisted Biocuration Workflows in Argo.
Rak, R., Batista-Navarro, R. T. B., Rowley, A., Carter, J. and
Ananiadou, S.
openminted.eu

19. Workflow Designer
19

20. Sample workflow (Cancer Mechanisms)
existing components
custom components
existing components supplied
with custom resources

21. ✓ highly extensible
✓ can be optimised by interchanging components
Sample machine reading workflow

22. 22
Annotation Viewer/Editor
This is where the footer goes

23. Ensemble reading through federation
• What do we gain by combining text mining
tools from different groups?
– enriching/updating results
– comparison
– merging annotations e.g., by taking the union,
intersection, majority vote
– taking advantage of best-of-breed tools
23

24. Federated system
IASON
Registry of services
Text mining
tool
developer
Text mining
tool as a web
service
Descriptor file
Text mining workflow
24

25. Deep Reading: Integrating uncertainty
• LitPathExplorer
– Visual analytics tool; maps events from literature to
pathway interactions
– Includes uncertainty measure
Soto, A., Zerva, C., Batista-Navarro, R., and S. Ananiadou (2017) LitPathExplorer
Bioinformatics

26. LitPathExplorer:
A Visual Tool for Exploring Literature-
Enriched Pathway Models
• Due to their size and complexity, pathway
models are typically neither complete nor
error-free
• Revising, updating models
• Curators and consumers of these models need
to contrast and revise large collections of
research articles
– Costly, time consuming
26

27. LitPathExplorer: a confidence-based
tool for exploring pathway models
1. Enabling flexible search and exploration of
biomolecular pathway networks
– different views of the data
– various interactive functionalities
2. Provide a means for making existing evidence in the
scientific literature available to support
corroboration
3. Facilitate the discovery of new interactions that are
not yet part of a given model
4. Allow the user to become an active participant of
the analytical process
quantify confidence
in the events
27
Soto, A. et al, 2017, Bioinformatics

28. 1. Search
• A pathway model can be
searched by providing:
– event types,
– entities,
– and/or roles for each entity in
the reaction
• Multiple queries can be
combined in a Boolean
search 28

29. 2. Network viewer
Reading against the model
29
Entities
Reactions/
Events
• Colour encodes event type
• Size encodes confidence

30. 30
3. Inspector, event confidence
computation
Mapping IDs for
entities and events
Overall event
confidence

31. 3. Inspector, quantifying the confidence
31
Confidence
breakdown

32. Adjusting event confidence
32

33. 4. Text Analyzer – Articles & sentences
33
Sentence-level language
confidence
Article-level language
confidence

34. Network Viewer: Discovery mode
Extending the model with events found in the
literature
34

35. 3
5
Discovery mode
Difficult to explore when too many candidate events are found

36. Verifying mentions in text
36

37. Summary
• Text mining important to overcome silos,
fragmentation of information
• Complex events in context and visualisation
can corroborate and extend models (deep
reading)
• Text mining infrastructure supporting
customisation
• Towards mechanisms and system
understanding
37

38. National Centre for Text Mining
• 1st publicly funded national text
mining centre
• Location: Manchester Institute of
Biotechnology, University of
Manchester
• Since 2004-
• Fully sustainable 2011-
• Biology, Medicine, Biodiversity
www.nactem.ac.uk
The National Centre for Text Mining
NaCTeM

39. Presented at the Global Pharma
R&D Informatics Congress.
To find out more, visit:
www.global-engage.com