PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Probabilistic Modeling and Machine Learning in Structural and Systems Biology
Juho Rousu, Samuel Kaski and Esko Ukkonen, ed. (2006) Series of Publications B, University of Helsinki, Department of Computer Science , Volume B-2006-4 . Helsinki University Press , Helsinki, Finland . ISBN 952-10-3277-4

Abstract

This book contains extended abstracts presented at the workshop Probabilistic Modeling and Machine Learning in Structural and Systems Biology, held in Tuusula, Finland from June 17 to 18, 2006. The workshop is a part of the Thematic Programme 'Learning with Complex and Structured Outputs' of PASCAL, an European research network of excellence. The ever-ongoing growth in the amount of biological data, the development of genome-wide measurement technologies, and the shift from the study of individual genes to systems view all contribute to the need to develop computational techniques for learning models from data. At the same time, the increase in available computational resources has enabled new, more realistic modeling methods to be adopted. In bioinformatics, most of the targets of interest deal with complex structured objects: sequences, 2D and 3D structures or interaction networks. In many cases these structures are naturally described by probabilistic graphical models, such as Hidden Markov Models, Conditional Random Fields or Bayesian Networks. Recently, approaches that combine Support Vector Machines and probabilistic models have been introduced (Fisher kernels, Max-margin Markov Networks, Structured SVM). These techniques benefit from efficient convex optimization approaches and thus are potentially well-scalable to large problems in bioinformatics. The increasing amount of high-throughput experimental data begins to enable the use of these advanced modelling methods in bioinformatics and systems biology. At the same time new computational challenges emerge. Statistical methods are required to process the data so that underlying potentially complex statistical patterns can be discerned from spurious patterns created by random effects. At its simplest this problem calls for data normalization and statistical hypothesis testing, in the more general case, one is required to select a model (e.g. gene network) that best explains the data. The aim of this workshop was to provide a broad look at the state of the art in the probabilistic modeling and machine learning methods involving biological structures and systems, and to bring together method developers and experimentalists working with the problems. The technical program of the workshop contained 14 oral presentations, 15 poster presentations and 4 invited lectures. The invited lectures were given by Tommi Jaakkola (CSAIL/MIT), Jukka Jernvall (University of Helsinki), Koji Tsuda (CBRC/AIST) and Ross D. King (University of Wales, Aberystwyth). The contributed submissions were reviewed by an international program committee: Florence d'Alch´e-Buc (Universit´e d'Evry-Val d'Essonne), Jaakko Astola (Tampere University of Technology), Nello Cristianini (UC Davis / University of Bristol), Liisa Holm (University of Helsinki), Mark Girolami (University of Glasgow), Samuel Kaski (Helsinki University of Technology),Matej Oresic (Technical Research Centre of Finland), Juho Rousu (University of Helsinki), Esko Ukkonen (Helsinki Institute for Information Technology) and Jean-Philippe Vert (Ecole des Mines de Paris). We are grateful for their input that ensured a high quality program. Moreover, we would like to thank Veli Mäkinen and Esa Pitkänen for the help in practical arrangements of the workshop. We would like to thank Helsinki Institute of Information Technology for its financial contribution that helped to make this workshop possible and the two universities, University of Helsinki and Helsinki University of Technology for providing infrastructure to the disposal of the workshop. The workshop was also supported in part by the IST programme of European Community, under the PASCAL Network of Excellence, IST-2002-506778. Helsinki, June, 2006 Juho Rousu, Samuel Kaski and Esko UkkonenI

EPrint Type:Book
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Multimodal Integration
Theory & Algorithms
Information Retrieval & Textual Information Access
ID Code:2273
Deposited By:Juho Rousu
Deposited On:13 October 2006