Logo Università di Pisa BITS Annual Meeting 2011 - June 20-22, Pisa, Italy Istituto di Informatica e Telematica, Consiglio Nazionale delle Ricerche, Pisa
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Tutorials


Tutorials are free of charge, but registration is required, either through the registration form, or by sending email to the conference contact email address.

Tutorial lectures on selected topics in Bioinformatics will be held at the Computer Science Department of the University of Pisa on June 22nd in the afternoon.

Lecturer 1: Prof. Giorgio Valentini (Università di Milano).

Title: Machine learning methods for gene function prediction

Abstract: In this tutorial we introduce the gene function prediction problem as a complex classification problem characterized by several challenging issues, such as the large number of functional classes, the multiple annotations for each gene/gene product, the hierarchical relationships between functional classes, the different annotation evidence, the availability of multiple sources of complex and noisy data, the lack of a univocal definition of negative examples.
The first attempts to computationally predict the function of genes or gene products were based on algorithms able to infer similarities between sequences. More recently several machine-learning based methods, able to exploit multiple sources of data from high-throughput biotechnologies have been proposed and applied to this challenging problem.
We briefly overview the main machine learning-based research lines on this topic, ranging from network-based label propagation methods, kernel methods for structured output spaces and hierarchical multi-label ensemble methods.
In particular we focus on hierarchical ensemble methods outlining their effectiveness and limitations, in the context of the Gene Ontology and FunCat taxonomies.

References:
  • I. Friedberg, Automated protein function prediction-the genomic challenge, Brief. Bioinformatics, vol. 7, pp. 225-242, 2006.
  • Z. Barutcuoglu, R. Schapire, and O. Troyanskaya, Hierarchical multi-label prediction of gene function, Bioinformatics, vol. 22, no. 7, 2006.
  • S. Mostafavi, et al. GeneMANIA: a real-time multiple association network integration algorithm for predicting gene function. Genome Biology, 9(S4), 2008.
  • A. Sokolov and A. Ben-Hur. Hierarchical classification of Gene Ontology terms using the GOstruct method. Journal of Bioinformatics and Computational Biology, 8(2), 2010.
  • G. Valentini, True Path Rule hierarchical ensembles for genome-wide gene function prediction, IEEE ACM Transactions on Computational Biology and Bioinformatics, vol.8 n.3 May/June 2011.

Lecturer 2 : Dr. Andrea Bracciali (University of Stirling)

Title: Formal Models in Systems Biology

Abstract: Papers like "Protein molecules as computational elements in living cells" [Bray, Nature 1995] and "Cells as computation" [Regev and Shapiro, Nature 2002] have put forward the idea that many aspects of living systems have a computational nature. Specifically, the complex network of interaction and information exchange that occurs within the biochemistry at the inter and intra cellular level, can be assimilated to the functioning of a distributed, interactive computational system. In the words of Bray, proteins are "functionally linked ... into biochemical 'circuits' that perform a variety of simple computational tasks including amplification, integration and information storage".
Under this perspective, it has appeared natural to employ the techniques used to model and analyse interactive computational systems to the realm of living organisms. Such a research trend aims at further developments within Systems Biology, the research area that approaches the study of the living organisms at a systemic level (see "Systems Biology: a brief overview" [Kitano, Science> 2002]). Computationally inspired formal models and analysis techniques are being used to carry out "in silico" experiments, which may often represent a cheaper, faster, more ethical, more easily measurable, and less constrained complement to the more traditional "in vitro/vivo" investigation.
This tutorial will briefly survey some of the formal techniques, particularly those originated from concurrency theory, which have been adopted, adapted and further developed for the research in Systems Biology. Starting from a historical perspective, the main ideas of the approach will be discussed and a few small examples practically worked out.