PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Machine Learning in Systems Biology. Proc. 3rd International Workshop
Saso Dzeroski, Pierre Geurts and Juho Rousu
(2009) Technical Report. Helsinki University Printing House, Helsinki, Finland.


Molecular biology and all the biomedical sciences are undergoing a true revolution as a result of the emergence and growing impact of a series of new disciplines and tools sharing the ’-omics’ suffix in their name. These include in particular genomics, transcriptomics, proteomics and metabolomics, devoted respectively to the examination of the entire systems of genes, transcripts, proteins and metabolites present in a given cell or tissue type. The availability of these new, highly effective tools for biological exploration is dramatically changing the way one performs research in at least two respects. First, the amount of available experimental data is not a limiting factor any more; on the contrary, there is a plethora of it. Given the research question, the challenge has shifted towards identifying the relevant pieces of information and making sense out of it (a ’data mining’ issue). Second, rather than focus on components in isolation, we can now try to understand how biological systems behave as a result of the integration and interaction between the individual components that one can now monitor simultaneously, so called ’systems biology’. Machine learning naturally appears as one of the main drivers of progress in this context, where most of the targets of interest deal with complex structured objects: sequences, 2D and 3D structures or interaction networks. At the same time bioinformatics and systems biology have already induced significant new developments of general interest in machine learning, for example in the context of learning with structured data, graph inference, semi- supervised learning, system identification, and novel combinations of optimization and learning algorithms. This book contains the scientific contributions presented at the Third International Workshop on Machine Learning in Systems Biology (MLSB’2009), held in Ljubljana, Slovenia from September 5 to 6, 2009. The workshop was organized as a core event of the PASCAL2 Network of Excellence, under the IST programme of European Union. The aim of the workshop was to contribute to the cross-fertilization between the research in machine learning methods and their applications to systems biology (i.e., complex biological and medical questions) by bringing together method developers and experimentalists. The technical program of the workshop consisted of invited lectures, oral presentations and poster presentations. Invited lectures were given by Diego di Bernardo, Roman Jerala, Nick Juty, Yannis Kalaidzidis, Ross D. King, and William Stafford Noble. Twelve oral presentations were given, for which extended abstracts (papers) are included in this book: these were selected from 18 submissions, each reviewed by three members of the scientific program committee. Twenty-two poster presentations were given, for which one-page abstracts are included here. We would like to thank all the people contributing to the technical programme, the scientific program committee, the local organizers and the sponsors for making the workshop possible. Ljubljana, September 2009 Saso Dzeroski, Pierre Geurts and Juho Rousu

EPrint Type:Monograph (Technical Report)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:5701
Deposited By:Juho Rousu
Deposited On:08 March 2010