Difference between revisions of "Phylogenetic Trees"

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|resarea=Machine Learning
|resarea=Machine Learning
|restopic=Information Geometry; Stocastic Optimization; Evolutionary Computation
|restopic=Information Geometry; Stocastic Optimization; Evolutionary Computation

Latest revision as of 00:57, 4 April 2012

Phylogenetic Trees
Image of the project Phylogenetic Trees
Corresponding Proposal: Statistical inference for phylogenetic trees
Coordinator: MatteoMatteucci (matteo.matteucci@polimi.it)
Tutor: LuigiMalago (malago@elet.polimi.it)
Students: RiccardoDesantis (effetti@gmail.com)
Research Area: Machine Learning
Research Topic: Information Geometry, Stocastic Optimization, Evolutionary Computation
Start: 2009/05/01
Status: Closed
Level: Bs
Type: Thesis

The project focus on the study, implementation, comparison and analysis of different statistical inference techniques for phylogenetic trees.

The aim of this project is to create a taxonomy of malicious software (aka malware), because new malwares are often related to the older ones, creating something like an evolutionary relationship between them.

The framework used is R, a software environment for statistical computing which already provide a set of classes implementhing those phylogenetic inference models and methods, good enough to start.

We already have got a very large amount of data about classified malwares (collected by a team of PoliMi researchers), so we just have to test how standard phylogenetic methods react to such an enormous group of data.


  1. Joseph Felsenstein. Inferring Phylogenies. Sinauer Associates, Inc., 2004.
  2. Barry G. Hall. Phylogenetic trees made easy: A How-To manual. Sinauer Associates, Inc., third edition, 2008.
  3. Masatoshi Nei and Sudhir Kumar. Molecular Evolution and Phylogenetics. Oxford University Press, 2000.
  4. Emmanuel Paradis. Analysis of Phylogenetics and Evolution with R. Springer, 2006.
  5. Charles Semple and Mike Steel. Phylogenetics. Oxford University Press, 2003.

Useful links