Computational Bioinformatics Laboratory


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

Seminar This Week

No Seminar This Week

Upcoming Seminars

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

Past Seminars

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.

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.