| Bioinformatics
|
Recent advances in biology have allowed us to collect vast amounts of
genetic, proteomic and biomedical data. While this data offers the
potential to help us understand the building blocks of life, and to
revolutionise medicine, analysing and understanding it poses immense
computational and statistical challenges. Our work in Bionformatics
includes modelling protein secondary and tertiary structure, analysis
of gene microarray data, protein-protein interactions, and biomarker
discovery.
Some relevant publications:
- Podtelezhnikov, A. A., Ghahramani, Z., Wild, D.L. (2006)
Learning about Protein Hydrogen Bonding by Minimizing Contrastive
Divergence.
Proteins: Structure, Function, and Bioinformatics. DOI:
10.1002/prot.21247, Published online 15 Nov 2006.
- Chu, W. Ghahramani, Z. and Wild D.L. (2004)
A Graphical Model for Protein Secondary
Structure Prediction.
In Proceedings of the Twenty-First
International Conference on Machine Learning
(ICML-2004). Morgan-Kaufmann, pp. 161-168.
- Chu, W., Ghahramani, Z. and Wild, D.L. (2006)
Bayesian Segmental Models with Multiple Sequence Alignment Profiles for
Protein Secondary Structure and Contact Map Prediction.
IEEE/ACM
Transactions on Computational Biology and Bioinformatics
- Chu, W., Ghahramani, Z., Krause, R., and Wild, D.L. (2006)
Identifying Protein Complexes in High-Throughput Protein Interaction
Screens using an Infinite Latent Feature Model.
In Altman et al
(Eds) BIOCOMPUTING 2006: Proceedings of the Pacific Symposium.
Maui, Hawaii, January 2006.
- Chu, W., Ghahramani, Z., Falciani, F., and Wild, D. L. (2005)
Biomarker Discovery
in Microarray Gene Expression Data with Gaussian
Processes.
Bioinformatics, 21(16):3385-3393.
[abstract] [official pdf file at Bioinformatics website]
- Beal, M.J., Falciani, F., Ghahramani, Z., Rangel, C., and Wild,
D. L. (2005)
A Bayesian approach
to reconstructing genetic regulatory networks with hidden
factors.
Bioinformatics 21(3):349-356.
- Chu, W., Ghahramani, Z. and Wild, D.L. (2004)
A
graphical model for protein secondary structure
prediction [pdf]. [ps]
[webserver]
[slides]
[poster]
In Twenty-first International Conference on Machine
Learning (ICML-04). Banff, Alberta, Canada.
- Rangel C. , Angus J. , Ghahramani Z. and Wild, D.L., (2005)
Modeling genetic regulatory networks using gene expression
profiling and state space models.
In Husmeier, D., Dybowski, R. and
Roberts, S. (Eds): Probabilistic Modelling in Bioinformatics and
Medical Informatics, pages 269--293. Springer Verlag.
- Rangel, C., Angus, J., Ghahramani, Z., Lioumi, M., Southeran,
E., Gaiba, A., Wild, D.L., Falciani, F. (2004)
Modeling T-cell
activation using gene expression profiling and state space
models.
Bioinformatics, 20: 1361-1372.
- Bourne, P.E., Allerston, C.K.J, Krebs, W., Li, W, Shinyalov,
I.N., Godzik, A., Friedberg, I., Liu, T., Wild, D.L., Hwang, S.,
Ghahramani, Z., Chen, L., and Westbrook, J. (2004)
The Status of
Structural Genomics Defined through the Analysis of Current Targets
and Structures.
Pacific Symposium on Biocomputing World
Scientific Publishing, Singapore, 9:375-386.
- Dubey, A., Hwang, S., Rangel, C., Rasmussen, C.E.,
Ghahramani, Z., and Wild, D.L. (2004)
Clustering Protein Sequence
and Structure Space with Infinite Gaussian Mixture Models.
Pacific
Symposium in Biocomputing World Scientific Publishing, Singapore,
9:399-410.
- Wild, D. L., Rasmussen, C. E., and Ghahramani, Z. (2002)
A Bayesian
approach to modeling uncertainty in gene expression
clusters. International Conference on Systems Biology (ICSB).
- Raval, A., Ghahramani, Z. and Wild, D.L. (2002)
A
Bayesian network model for protein fold and remote homologue
recognition.
Bioinformatics 18(6): 788--801.
- Wild, D.L., Raval, A. and Ghahramani, Z. (2000)
A Bayesian
network model for protein fold and remote homologue recognition.
Eighth International Conference on Intelligent Systems for Molecular
Biology (ISMB '00). La Jolla, CA, August, 2000.