[Solomonov Seminar] 285. Solomonov seminar

[Marko Grobelnik]

V torek, 30. januarja 2018 bo ob 14h v Veliki predavalnici IJS 285. 
Solomonov seminar. Velika predavalnica je v prvem nadstropju glavne 
stavbe IJS na Jamovi 39. Posnetki preteklih seminarjev so na 
http://videolectures.net/solomon/

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Sophia Ananiadou, The University of Manchester

TITLE: Enriching Pathways using Text Mining

ABSTRACT:  Pathway models are valuable resources that help us to
understand the various mechanisms underpinning complex biological
processes. Their curation is typically carried out through manual
inspection of the scientific literature, a knowledge-intensive and
laborious task. Text mining methods are used to automate model
reconstruction by increasing the speed and reliability of discovery
and extracting evidence from the literature . Complex information
from the literature is automatically extracted and then mapped to
reactions in existing pathway models.  Information from the literature
(events) can act as corroborative evidence of the validity of these 
reactions
in a model or help to extend it. In addition, by contextualising the
textual evidence (extracting uncertainty, negation), we can provide
additional confidence measures for linking and ranking information
from the literature for model curation and ultimately experimental
design. In addition, visual analytics methods can act as the nexus
between text mining methods and modellers by providing an
interactive way to explore and analyse the statements linked with pathways.

SHORT BIO:
Sophia Ananiadou is Professor in Computer Science, School of Computer
Science, The University of Manchester and Director of the National
Centre for Text Mining.  Since 2005, she has successfully directed
NaCTeM to be currently a fully sustainable centre, carrying out novel,
world-leading research on text mining that then informs the
provision of services, tools, resources and infrastructure to a variety
of users from translational medicine, biology, biodiversity, humanities,
health, and social sciences. Research she has led has advanced the
state of the art in text mining and contributed in novel ways to:
automatic extraction of terminology and term variation; development of
robust taggers for biomedical text; automatic extraction of events
and their interpretation using machine learning methods; development of
large scale terminological resources for biomedicine and biodiversity;
linking textual evidence with metabolic and signaling pathways;
association mining and hypothesis generation; supporting the
development of systematic reviews using novel topic modeling and
clustering methods and the development of interoperable text mining
infrastructure to facilitate all the above applications (Argo).  Her
team achieved top performance is several text mining challenges,
e.g. BioCreaTive (2010, 2013, 2015), BioNLP (2011, 2013).