Improving algorithms in phylogenetics using machine learning

Improving algorithms in phylogenetics using machine learning

Versendaal, Bryan (TU Delft Applied Sciences; TU Delft Electrical Engineering, Mathematics and Computer Science)

van Iersel, L.J.J. (mentor)
Aardal, K.I. (graduation committee)
Dubbeldam, J.L.A. (graduation committee)
Jones, M. (graduation committee)

Delft University of Technology

Applied Mathematics

2020-09-11

In this thesis we look at three different algorithms within the field of phylogenetics and create a proof of concept for using machine learning to improve the algorithms. The problems are the maximum agreement forest problem, the hybridization number problem and finally the tail move problem. A study of the problems show that they can all benefit from machine learning. For each of the problems we find a machine leanring implementation using basic decision trees. The results show that the MAF problem and the tail move problem can both benefit from machine learning and for the hybridization number problem we give an implementation that is faster than the studied algorithm.

Phyolgenetics
Machine Learning
Maximum Agreement Forest
Hybridisation Number Problem
Tail-Move Problem
Optimisation

http://resolver.tudelft.nl/uuid:83690896-2783-4340-8b02-11a23d82e9f9

https://github.com/TUbryan/PhyloThesis This page contains the code that was used during the thesis.

Student theses

master thesis

© 2020 Bryan Versendaal