Alberto Paccanaro

Reader in Computational Biology

Centre for Systems and Synthetic Biology &
Department of Computer Science
Royal Holloway, University of London

To answer many important questions in biology we need to integrate diverse sets of data from different sources, and these data sets are often large and very noisy. My research interests are in applying and developing novel computational methods that make use of these diverse biological data sets to answer biological questions. Particularly, the focus of my research is in developing machine learning and pattern recognition techniques for solving problems in molecular computational biology. Such techniques can provide an answer to many of the current challenges in the field because they offer a natural way to integrate different types of data and to handle large amounts of noisy information.

This is my webpage. If you are looking for my Lab page, you can find it here

                                    

Publications

Current Research Group Haixuan Yang Tamas Nepusz Dimitrios Zervas Prajwal Bhat Sandra Smieszek Frens Tedeschini

Recent Research Projects

Spectral methods for clustering protein sequences (more...)
Techniques for integrating different protein-protein interaction experiments (more...)
Methods for denoising large scale protein-protein interaction experiments (more...)
Protein-protein interaction prediction (more...)
Prediction of gene essentiality from genomic features (more...)
Prediction of protein function in E. Coli (more...)
Quantifying environmental adaptation of metabolic pathways in metagenomics (more...)

Global functional atlas of Escherichia coli encompassing previously uncharacterized proteins, PLoS Biol. 2009 Apr 28;7(4)
Quantifying environmental adaptation of metabolic pathways in metagenomics, Proc Natl Acad Sci U S A. 2009 Feb 3;106(5):1374-9.
A Maximal Eigenvalue Method for Detecting Process Representative Genes by Integrating Data from Multiple Sources, NIPS 2008 -- worshop  on Learning from Multiple Sources
Signal Transduction Networks During Stress Responses in Arabidopsis: High-Throughput Analysis and Modelling, in Bögre L and Beemster G. Eds, Plant Growth Signaling, Plant Cell Monographs series, Vol. 10, Springer, 2008
Statistical analysis of the genomic distribution and correlation of regulatory elements in the ENCODE regions, Genome Research, 2007,17(6):787-97
Global landscape of protein complexes in the yeast Saccharomyces cerevisiae, Nature, 2006, Mar 30,440(7084):637-43
Spectral Clustering of Proteins Sequences, Nucleic Acids Research 2006, Mar 17;34(5):1571-80
Predicting Essential Genes in Fungal Genomes, Genome Research, 2006, 16 (9): 1126-35
Clustering of Pseudomonas aeruginosa transcriptomes from planktonic cultures, developing and mature biofilms, BMC Genomics, 2006, 7:162
Predicting interactions in protein networks by completing defective cliques, Bioinformatics 2006, Apr 1;22(7):823-9
Integration of curated databases to identify genotype-phenotype associations, BMC Genomics. 2006, Oct 12;7:257
Assessing the Limits of Genomic Data Integration for Protein-Protein Interactions, Genome Research, Jul 2005, 15: 945 – 953


Current Lab Funding

- Overlapping community detection methods for biological applications
  Newton International Fellowship, £ 98,000 started in February 2009
- Development of graph theoretic approaches to predict protein function by integrating large scale heterogeneous data
  BBSRC New Investigator Grant, £ 517,000 started in October 2008
- BioSynLab: a software platform for the analysis of metabolic data
  Park (Partnership in Accessible Research and Knowledge), £ 53,202 started in October 2007
- A pilot study of graph theoretic approaches for predicting protein-protein interactions
  Royal Holloway Research Strategy Fund, £5350

Dr Alberto Paccanaro
Department of Computer Science
Royal Holloway, University of London
Egham, TW20 0EX
UK

Phone: +44 1784 414239
Fax: +44 1784 439786
Email:  alberto - at - cs.rhul.ac.uk
(replace -at- with @ )