The interest of statistical physics for combinatorial optimization is not new, it suffices to think of a famous tool as simulated annealing. Recently, it has also resorted to statistical inference to address some "hard" optimization problems, developing a new class of message passing algorithms. Three applications to computational biology are presented in this thesis, namely: 1) Boolean networks, a model for gene regulatory networks; 2) haplotype inference, to study the genetic information present in a population; 3) clustering, a general machine learning tool.
|Titolo:||Statistical physics methods in computational biology|
|Relatore/i esterni:||Zecchina, Riccardo|
|Data di pubblicazione:||3-lug-2007|
|Appare nelle tipologie:||8.1 PhD thesis|