[Solomonov Seminar] 220. Solomonov seminar

[Marko Grobelnik]

V sredo 7. aprila bo ob 13:00h v Oranzni predavalnici IJS
(drugo nadstropje glavne zgradbe), 220. Solomonov seminar.
Posnetki preteklih seminarjev so na http://videolectures.net/solomon/

Predaval bo Stephen Muggleton iz Imperial College iz podrocja
uceja na bioloskih mrez.

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Stephen Muggleton, Imperial College, London, UK
       Machine Learning Biological Network Models

In this talk we survey work being conducted at the Centre for
Integrative Systems Biology at Imperial College on the use of
machine learning to build models of biochemical pathways.
Within the area of Systems Biology these models provide
graph-based descriptions of bio-molecular interactions which
describe cellular activities such as gene regulation, metabolism
and transcription.  One of the key advantages of the approach taken,
Inductive Logic Programming, is the availability of background knowledge
on existing known biochemical networks from publicly available
resources such as KEGG and Biocyc.  The topic has clear societal impact
owing to its application in Biology and Medicine. Moreover, object
descriptions in this domain have an inherently relational structure in the
form of spatial and temporal interactions of the molecules involved.
The relationships include biochemical reactions in which one set
of metabolites is transformed to another mediated by the involvement
of an enzyme.  Existing genomic information is very incomplete
concerning the functions and even the existence of genes and
metabolites, leading to the necessity of techniques such as
logical abduction to introduce novel functions and invent
new objects. Moreover, the development of active learning
algorithms has allowed automatic suggestion of new experiments
to test novel hypotheses. The approach thus provides support
for the overall scientific cycle of hypothesis generation and
experimental testing.