Seminars
Computational Bioinformatics Group holds weekly seminars wherer invited speakers and group members present their work.
The seminars take place on Wednesday 12-1pm.
All are welcome. If you would like to give a talk, please contact
Dianhuan Lin
Seminar This Week
Upcoming Seminars
Past Seminars
No Seminar This Week
Date |
Venue |
Speaker |
Title and Abstract |
9 Feb 2011 |
344A |
Robert Henderson |
TBA |
16 Feb 2011 |
344A |
Hiroaki Watanabe |
TBA |
23 Feb 2011 |
217/218 |
Alireza Tamaddoni-Nezhad |
TBA |
2 March 2011 |
217/218 |
Dianhuan Lin |
TBA |
9 March 2011 |
217/218 |
Omer Nebil Yaveroglu |
TBA |
16 March 2011 |
217/218 |
Niels Pahlavi |
TBA |
23 March 2011 |
217/218 |
Jianzhong Chen |
TBA |
30 March 2011 |
217/218 |
Kai Sun |
TBA |
24 Nov 2010
Room 343
|
Speaker: Stephen Muggleton
Title: Game-theoretic logic learning in scientific domains
Abstract: This talk is motivated by an attempt to model the process of automated
scientific discovery in terms of the theory of competitive and collaborative
games. For instance, at the object-level within Systems Biology interactions
between a host and pathogen can be modelled as a form of adaptive competition.
Conversely at the meta-level groups of experimental scientists can be viewed
as conducting a collaborative game involving the proposal and refutation of
hypotheses by experimentation. Some relevant concepts from Game Theory are
briefly reviewed. We then introduce a formalism called Game-Theoretic Logic
Programs (GTLPs), which allow modelling of multi-player strategies based on
an adaptation of McCarthy's situation calculus. Lastly we propose an approach
to machine learning such strategies. We argue that the approach, called
Pre-emptive Strategy Learning (PSL), represents a departure from traditional
forms of Machine Learning. Usually Machine Learning is conceived in terms of
extracting patterns from a database of past experiences with the aim of
predicting future outcomes. By contrast, experimental choices in new areas
of science might be compared to the problem BP faced during the oil spillage
in the Caribbean. The absence of relevent historical data dictates a new
approach. It is envisaged that PSL will machine learn strategies by sampling
projected future events from a GTLP description of actions with associated
probabilities and costs. In this way, machine learning could be applied
iteratively within cycles of experiment planning. Although the development
of GTLPs and PSL would have direct and immediate effect within automated
scientific discovery tasks, we believe that the approach should also have
broader application within other areas of computer science in which
situations and actions have associated uncertainties and costs.
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10 Nov 2010
Room 217-218
|
Speaker: Natasa Przulj
Title: Complementarity of network and sequence information in
homologous proteins
Abstract: Traditional approaches for homology detection rely on finding sufficient similarities between
protein sequences. Motivated by studies demonstrating that from non-sequence
based sources of biological information, such as the secondary or tertiary molecular structure,
we can extract certain types of biological knowledge when sequence-based approaches
fail, we hypothesize that protein-protein interaction (PPI) network topology and protein sequence
might give insights into different slices of biological information. Since proteins
aggregate to perform a function instead of acting in isolation, analyzing complex wirings
around a protein in a PPI network could give deeper insights into the protein's role in the
inner working of the cell than analyzing sequences of individual genes. Hence, we believe
that one could lose much information by focusing on sequence information alone.
We examine whether the information about homologous proteins captured by PPI network
topology differs and to what extent from the information captured by their sequences. We
measure how similar the topology around homologous proteins in a PPI network is and
show that such proteins have statistically significantly higher network similarity than nonhomologous
proteins. We compare these network similarity trends of homologous proteins
with the trends in their sequence identity and find that network similarities uncover almost
as much homology as sequence identities. Although none of the two methods, network
topology and sequence identity, seems to capture homology information in its entirety, we
demonstrate that the two might give insights into somewhat different types of biological
information, as the overlap of the homology information that they uncover is relatively
low. Therefore, we conclude that similarities of proteins' topological neighborhoods in
a PPI network could be used as a complementary method to sequence-based approaches
for identifying homologs, as well as for analyzing evolutionary distance and functional
divergence of homologous proteins.
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