Statistical and Machine Learning Methods in Computational Biology

June 12 - June 19, 2010


Aim and scope: The theme of the School: Statistical and Machine Learning Methods in Computational Biology is among the most representative areas of interdisciplinary research in modern science. It has also a very long and prestigious history of fundamental contributions to biology and medicine. However, since high throughput and genome wide experiments are becoming a standard for biological investigation, new techniques for the analysis of biological data are required. The main lectures will focus on four aspects of the theme, ranging from new statistical challenges posed by deep sequencing techniques to inference and analysis of network structure that take into full account the paradigm shift that we have witnessed in terms of the scale of data available. A series of tutorial is also offered, with the intent to complement the main lectures by providing snapshots of other areas that are perceived of relevance to the theme of the School. They range from introductory topics to Statistics to probabilistic and Machine Learning methods used to shed light on nucleosome organization in genomes. As it is clear from the enclosed bibliography, the selected themes have received much attention in the scholarly literature that ranges from Science to BMC Bioinformatics and Bioinformatics. As a whole, the planned summer school will allow young researchers interested in bioinformatics and biomedicine to be exposed to cutting edge results in an area that, although classic, is among the most effervescent in Post-Genomic Biology.


  • Silvio Bicciato
    An Overview of Statistical Tests for the Identification of Differentially expressed genes [abstract]
    University of Modena and Reggio Emilia

  • Charles Lawrence
    The two sides of genomic statistical Inference [abstract]
    Brown University, Providence, USA

  • Dana Pe’er
    Computational Learning Methods and Biological Networks [abstract]
    Columbia University In the City of New York, USA

  • Catarina Sismeiro
    A Primer of Statistical Techniques [abstract]
    Imperial College, London, UK

Guest lectures

  • Mary Ellen Bock
    Statistical Challanges in the Analysis of Massive Datasets in Bioinformatics [abstract]
    Purdue University,West Lafayette, IN, USA


  • Alfredo Pulvirenti
    Two and Multiple Sequence Alignment [abstract]
    University of Catania, Italy

  • Matteo Comin
    Phylogenetic methods for biological sequences [abstract]
    University of Padova, Italy

  • Raffaele Giancarlo
    Computational Cluster Validation for Post Genomic Data Analysis [abstract]
    University of Palermo, Italy

  • Giosuè Lo Bosco
    Machine Learning Methods for Nucleosome Positioning [abstract]
    University of Palermo, Italy

  • Concettina Guerra
    Title: to be annunced
    University of Padova and Georgia Tech

School Directors

  • Prof. Alfredo Ferro (University of Catania)
  • Prof. Raffaele Giancarlo (University of Palermo)
  • Prof. Concettina Guerra (University of Padova and Georgia Tech.)
  • Prof. Michael Levitt, (Stanford University)

  • Dr. Rosalba Giugno (co-director, University of Catania)
  • Dr. Alfredo Pulvirenti (co-director, University of Catania)