|Corresponding Proposal:||Statistical inference for phylogenetic trees|
|Research Area:||Machine Learning|
|Research Topic:||Information Geometry, Stocastic Optimization, Evolutionary Computation|
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.
- Joseph Felsenstein. Inferring Phylogenies. Sinauer Associates, Inc., 2004.
- Barry G. Hall. Phylogenetic trees made easy: A How-To manual. Sinauer Associates, Inc., third edition, 2008.
- Masatoshi Nei and Sudhir Kumar. Molecular Evolution and Phylogenetics. Oxford University Press, 2000.
- Emmanuel Paradis. Analysis of Phylogenetics and Evolution with R. Springer, 2006.
- Charles Semple and Mike Steel. Phylogenetics. Oxford University Press, 2003.