Arief Gusnanto
My main research interest is in statistical inference of high-dimensional data in molecular biology, genetics, gene and protein expression. I am currently working in a project to discover genetic markers for the prediction of thrombus formation in coronary artery disease. More information can be obtained from my personal website.
Medical Research Council – Biostatistics Unit
Institute of Public Health
Robinson Way
Cambridge CB2 0SR
United Kingdom
Tel. +44 1223 767408
Fax. +44 1223 330388
Email Arief.Gusnanto@mrc-bsu.cam.ac.uk
http://www.mrc-bsu.cam.ac.uk/personal/arief

Magnus Åberg
Research Activity:
My research is mainly focussed on method/algorithm development for data analysis in the field of analytical chemistry in general, and metabolic profiling in specific. In the metabolic profiling and other information rich measurements there is a need to automate as much of the data analysis as possible. For instance, it is not feasible to manually integrate all the peaks in full-scan LC/MS runs on several hundred samples; there are thousands of peaks in each sample. The part of the data analysis that needs most to be automated is the pre-processing step where the data are transformed so that every variable carries the same type of information in all the samples. With the NMR and LC/MS platforms this is not generally true; the locations of the peaks shift about due to physicochemical differences between samples and instrumental irreproducibility. Warping and peak alignment are methods that solves the problem with peak shifts by using the local pattern to assign correspondence between the variables in different samples.
Complete data analysis of a metabolic profiling dataset includes, in addition to warping or peak alignment; peak detection, baseline estimation and some statistical evaluation, for instance using Bayesian modelling, at the end. My research addresses all these issues.
Teaching:
Lecturer and lab assistant at the Chemometrics course at Stockholm University, yearly since 2000. The course teaches basic statistics and chemometrics, with focus on design of experiments, principal component analysis (PCA) and partial least squares regression (PLS).
Stockholm University
Dept. of Analytical chemistry
106 91 Stockholm
Sweden
Phone: +46-8-16 24 35
Email: magnus.aberg@anchem.su.se

Luisa Bernardinelli
MRC Biostatistics Unit
Institute of Public Health
Forvie Site, Robinson Way
Cambridge CB2 2SR
United Kingdom
Ph. +44 (1223) 330300
Fax +44 (1223) 330388
Dipartimento di Scienze Sanitarie Applicate
Università di Pavia

Carlo Berzuini
MRC Biostatistics Unit
Institute of Public Health
Forvie Site, Robinson Way
Cambridge CB2 2SR
United Kingdom
Ph. +44 (1223) 330300
Fax +44 (1223) 330388
Dipartimento di Informatica e Sistemistica
Università di Pavia
Via Ferrata 1, 27100 Pavia

Eduwin Pakpahan
Dipartimento di Scienze Sanitarie Applicate
Università di Pavia
Via Bassi 21, 27100 Pavia
Email: eduwin.pakpahan@unipv.it

Chris Holmes
I am lecturer in Statistical Genomics at the University of Oxford, based in the Oxford Centre for Gene Function. My research concerns the theory, methods and applications of statistical modelling to the genomic sciences, with particular interest in Bayesian statistics and genetic epidemiology. I teach a number of post-graduate courses in Oxford on statistical bioinformatics.
Department of Statistics
University of Oxford
Oxford Centre for Gene Function
Mammalian Genetic Unit MRC | Harwell
http://www.stats.ox.ac.uk/~cholmes/

Marc-Emmanuel Dumas
Marc-Emmanuel Dumas is a Wellcome Trust Research Fellow in the Department of Biomolecular Medicine, Imperial College London (UK). He has been involved in all aspects of metabonomics data acquisition and analysis since 1998. On the analytical side, he has focussed on promoting multidimensional nuclear magnetic resonance (NMR) and metastable atom bombardment mass spectrometry (MAB-MS) metabonomic strategies in doping control during his PhD. On the statistical analysis side, he has developed feature selection models, pattern recognition models for epidemiology and insulin resistance research. His research interests focus on applications of metabonomics to systems biology, chemical biology and genetical genomics. He has been leading the metabonomics workpackage of the Wellcome Trust funded Biological Atlas of Insulin Resistance consortium since 2002. Marc has been involved in several courses on metabonomics and will start his research group as associate professor position in the new Ultra-High Field NMR centre at the Department of Chemistry of Ecole Normale Supérieure de Lyon (Fr) in April 2007.
Department of Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics, Faculty of Medicine, Sir Alexander Fleming Building, Exhibition Road, South Kensington, London SW7 2AZ, United Kingdom.
Phone: +44 207 594 1698
Fax: +44 207 594 3226
Email: m.dumas@imperial.ac.uk
http://www1.imperial.ac.uk/medicine/people/m.dumas/

Mattias Rantalainen
Mattias Rantalainen got a MSc in Engineering Biology (2003) at Umea University. He is currently a PhD student at the Department of Biomolecular Medicine, Imperial College London. His thesis research includes algorithm development and application of multivariate statistical models for integration, prediction and visualization of ``Omics’’ data.
Department of Biomolecular Medicine
Division of Surgery, Oncology, Reproductive Biology and Anaesthetics (SORA)
Faculty of Medicine
Imperial College of Sciences, Technology and Medicine
Sir Alexander Fleming Building
Exhibition Road
South Kensington
London SW7 2AZ
Email:mattias.rantalainen (at) imperial.ac.uk

Jens Lamerz
Jens Lamerz worked on the biostatistical analysis of quantitative
Proteomics/Peptidomics data for the last five years. In this time, he
contributed to the implementation of data integration, data processing,
quality control and Biomarker Discovery. In his time at BioVisioN AG,
Hannover / ETH Zurich, his primary focus was the interpretation of
correlated signals in Peptidomics data and its use for Sequence algorithms and Biomarker panels. He currently analyses bioassay features of SILAC data at Roche RCMG.
Jens Lamerz, PhD,
F. Hoffmann-La Roche AG
Roche Center for Medical Genomics (RCMG)
Application Development BR
Bldg. 93 / 3.44
CH-4070 Basel
Tel. +41 61 6875183
Fax: +41 61 6881490
e-mail: jens.lamerz@roche.com

Philip Brown
Phil Brown is Pfizer Professor of Medical Statistics and Head of the
Statistics Group at the University of Kent. He is also Honorary Associate
in the Faculty of Medicine, Monash University, Melbourne Australia.
His PhD was in the area of multivariate analysis applied to drug discovery and was completed whilst he was supported by Imperial Chemical Industries. Since then he has worked extensively on chemometric problems culminating in a book on regression and multivariate calibration in 1993. Most recently he has been working
with researchers from the MD Anderson Cancer Center in Houston on Mass Spectroscopy applied to proteomic data. He has taught at undergraduate and graduate level in Imperial College, Liverpool University and University of Kent in the UK. He has taught also in Berkeley (California), Helsinki, Stockholm and Rome. International Honours include membership of the Institute of Mathematical Statistics, Fellowship of American Statistical Association and in 2003 he was recipient, with three co-authors, of the Mitchell Prize for the best
applied Bayesian paper. He is on the Editorial advisory Board of Chemometrics and Intelligent Laboratory Systems.
Philip J Brown, Pfizer Professor of Medical Statistics, Institute of Maths,
Statistics and Actuarial Research, University of Kent, CT2 7NF, UK.
Email pjb8@kent.ac.uk
http://www.kent.ac.uk/ims/groups/statistics/members.htm

Richard H. Barton
Richard is currently project leader for the Metabonomics platform of the Centre for Integrated Systems Biology at Imperial College (CISBIC). After a first degree in chemistry, he spent some years studying psychology and philosophy of science and teaching logic, before returning to chemistry to model electrode surface kinetics. This was followed by a period of research in physical and analytical chemistry and an AGMARDT PhD scholarship project involving development of techniques in quantitative 13C NMR spectroscopy whilst in New Zealand. Subsequently, after working in the UK Biotech industry and freelancing in Paris, he took a position in what is now the Biomolecular Medicine section of the Faculty of Medicine at Imperial College, London. In this role, he has been involved in the ongoing development of Metabonomic technologies, specifically including work in NMR-based data acquisition, signal correction, and data filtering techniques aimed at enhancing information recovery from high-field NMR biofluid spectroscopic data. Projects include modelling in analytical methodology, toxicity prediction, molecular epidemiology, and disease pathogenesis with particular reference to Type II Diabetes with the Wellcome Trust Biological Atlas of Insulin Resistance consortium project.
Research Fellow
Biomolecular Medicine
Faculty of Medicine
and
Metabonomics platform project leader,
Centre for Integrated Systems Biology at Imperial College (CISBIC)
Imperial College London
Sir Alexander Fleming Building
Exhibition Road,
London SW7 2AZ
United Kingdom.
Phone: +44 207 594 3014
Fax: +44 207 594 3226
Email: r.barton@ic.ac.uk

Bart Mertens
Teaching:
Currently coordinating and teaching the course Clinical Research in
Practice for fourth year (Masters) students in Biomedical Science. The
course focuses on logistic regression and survival analysis in a strong
interdisciplinary context (joint with Dep. Epidemiology and Medical
Information Science).
Research:
My two main research interests at the moment are:
* development of statistical methods for diagnosis and prediction in
mass spectroscopy proteomics
* quality control and comparison methods in radiology
General research interest are in Bayesian methods, classification and
discriminant analysis, prediction, diagnosis and (cross-)validatory
analysis.
Department of Medical Statistics and Bioinformatics
Leiden University Medical Centre
Leiden University
The Netherlands
Visiting Address: LUMC, Einthovenweg 20, Leiden,
Postal Address: LUMC (Postal Zone S5-P), PO Box 9600, 2333 ZC Leiden
The Netherlands
Tel: +31 (0)71-526.9706
Fax: +31 (0)71-526.8280
Email address: b.mertens@lumc.nl
Websites:
www.msbi.nl/mertens
www.lumc.nl

Cinzia Stella
Cinzia Stella is currently a lecturer at the Laboratory of Pharmaceutics and Bioinformatics at the University of Geneva (EPGL) – Switzerland. She obtained her PhD in 2003 in Pharmaceutical Analytical Chemistry at the University of Geneva and held a research associate position at Imperial College – London (UK) in the field of metabonomics. Her main expertise is the use of analytical tools, such as High (Ultra) Performance Liquid Chromatography and High Resolution Nuclear Magnetic Resonance Spectroscopy, for the analysis of biofluids and pharmaceutical formulations. She has been involved in several collaborative projects between industry and academia concerning the metabolic characterization by H(U)PLC coupled to Mass Spectrometry of human biofluids, mainly in nutritional studies.
Her teaching experience is the application of analytical and statistical tools for Pharmacy students and metabonomic techniques for Medicine students.
Cinzia Stella, PhD
Laboratory of Pharmaceutics and Biopharmaceutics
EPGL – University of Geneva University of Lausanne
Quai Ernest Ansermet 30
CH-1211 Geneva 4 / Switzerland
Tel. : +41 (0)22 379 68 89
Fax : +41 (0)22 379 65 67
e-mail : Cinzia.Stella@pharm.unige.ch

Anthony Maher
My research is mainly focussed on the application of NMR spectroscopy to address biological questions. At present I am working at Imperial College London on the metabonomics workpackage (WP2) of the MolPAGE project. My role is to run 1H NMR experiments on urine and plasma samples as part of a coordinated effort to understand type II diabetes on a systems biology level.
Imperial College London
e-mail: a.maher@imperial.ac.uk

Mark Earll
Mark Earll is a consultant for Umetrics UK Ltd a software company specialising in the visualisation and modelling of research data using chemometric methods. Customers include major pharmaceutical companies and academic researchers worldwide. Mark has previously worked in pharmaceutical R&D as an analytical chemist with interests in QSAR and ADMET modelling. Since joining Umetrics he has worked with the application of chemometrics methods to "Omics" data for both academic and industrial clients.
Umetrics UK Ltd
Woodside House
Woodside
Winkfield
Berkshire
SL4 2DX
e-mail : mark.earll@umetrics.com.uk

Bernard Silverman
Bernard Silverman is Master of St Peter's College, Oxford and
Professor of Statistics at Oxford University. His research ranges widely over areas of methodological, theoretical and practical statistics, but his main areas of interest are in smoothing methods, wavelets in statistics, functional data analysis and, the subject of the current talk, empirical Bayes methods for very high dimensional problems.
Oxford University
email: bernard.silverman@spc.ox.ac.uk
web: www.bernardsilverman.com

Brian D M Tom
Brian Tom is a statistician at the Medical Research Council Biostatistics Unit since December 2001. His research areas are quite varied, ranging from epidemiology to biostatistics and bioinformatics.He is currently involved in projects on chronic diseases such as hepatitis C virus, psoriatic arthritis, mental illnesses and cardiovascular disease. His interests in the area of bioinformatics, have been mainly in the area of microarrays (analysis, quality determination, and data fusion), although he is presently involved on projects to do with pseudo-genes and transcription binding site prediction. He is part of the statistical team involved in the EU funded Bloodomics consortium whose aim is to investigate the role of platelets and other blood cells in heart disease.
Medical Research Council Biostatistics Unit Institute of Public Health University Forvie Site Robinson Way
Cambridge CB2 2SR
United Kingdom
Tel no: +44 1223 330 395
Email : brian.tom@mrc-bsu.cam.ac.uk

Paul Eilers
I’m an associate professor at the Methodology and Statistics department of Utrecht University, but until very recently I worked at the Department of Medical Statistics and Bioinformatics of the Leiden University Medical Centre. I have been working with hig-throughput data (expression microarrys, mass spectrometry, SNP, NMR) for the last seven years. Being educated as an electronic engineer I have always had a strong interest in signal processing. In many consulting projects I have worked on a variety of (high-volume) chemical and physical measuremnts, from laboratories or from the field. This has turned out to be very useful in the bioinformatics arena: where data quality can be a real problem (although this is often ignored by eager users). It has also led to a number of papers in statistics, bioinformatics and chemometrics journals, describing practical algorithms.
Dr. ing. Paul H. C. Eilers
tel. (31) 30 253 4438
fax (31) 30 253 5797
e-mail: p.h.c.eilers@fss.uu.nl
Visiting address:
Kamer E347
Martinus Langeveldgebouw
Heidelberglaan 1
3584 CS Utrecht
Postal address (English)
Methodology and Statistics
Faculty of Social and Behavioural Sciences
Utrecht University
P.O. Box 80140
3508 TC Utrecht
The Netherlands
Postal address (Dutch)
Disciplinegroep Methoden & Technieken
Faculteit Sociale Wetenschappen
Universiteit Utrecht
Postbus 80140
3508 TC Utrecht

Nathan Edwards
Dr. Edwards received a Ph.D. in Operations Research from Cornell University in 2001. Joining the Informatics Research group at Celera Genomics, Dr. Edwards worked on SCOPE, for identifying peptides from tandem mass spectra by searching protein sequence databases, and other critical elements of the analysis infrastructure for Celera's high-throughput proteomics facility. Moving to Applied Biosystems, still as part of the Informatics Research group in 2002, he led research on algorithmic and statistical issues arising in the analysis of proteomics biomarker workflows and developed the Biomarker Toolbox prototype.
Since joining the Center for Bioinformatics and Computational Biology at the University of Maryland, College Park, in 2004, Dr. Edwards' research has focused on the discovery of novel peptides that characterize alternative splicing, coding SNPs, and mutant protein isoforms, using genomic and EST sequences; and on the rapid identification of microorganisms by MALDI-TOF mass spectrometry and bioinformatics, in collaboration with researchers at University of Maryland, College Park; and the Johns Hopkins School of Public Health.
Dr Edwards co-teaches a course in Biological Mass Spectrometry with Dr. Catherine Fenselau and co-directed a USHUPO short course, with Dr.
Akilesh Pandey, in 2005 and 2006, titled Bioinformatics for Proteomics.
Nathan Edwards, Ph.D.
Center for Bioinformatics and Computational Biology University of Maryland, College Park, MD 20742
Email: nedwards@umiacs.umd.edu

George Nicholson
I am a postdoctoral researcher in the data analysis section of MolPAGE. Our role in MolPAGE is to analyse data sets produced from a wide variety of molecular phenotyping platforms (e.g. Affymetrix gene expression arrays, metabonomic NMR data, proteomic mass spectrometry data). Our statistical remit can, broadly speaking, be split in two. Our first goal is to characterise variation in data on a platform-specific level. Understanding the sources of variability (e.g. genetic, biological, environmental and experimental) inherent in the measurement of a molecular phenotype is a key step in assessing the potential for stable, informative biomarkers. Our second aim is that of biomarker discovery itself. Analysis of data from samples drawn from large cohort studies will allow the comparison of molecular profiles of individuals that are discordant for diabetes-related clinical traits. We aim to discover molecular signatures that are informative for diabetes diagnosis and prognosis. Cross-platform data integration will feature prominently in the search for powerful and stable biomarkers.
Department of Statistics, University of Oxford,
Email: nicholso@stats.ox.ac.uk

Ingileif B. Hallgrimsdottir
I am a post-doctoral researcher in the Statistics Department at the
University of Oxford. My research is focused on methods for
identification of biomarkers for disease in the context of proteomics and
metabonomics. My background is in statistical genetics and I also work on problems in genomewide association analysis.
Department of Statistics
University of Oxford
e-mail: ingileif@stats.ox.ac.uk