Traditional algorithms for hidden Markov model decoding seek to maximize
either the probability of a state path or the number of positions of a sequence
assigned to the correct state. These algorithms provide only a single answer and
in practice do not produce good results. The most mathematically sound of these
algorithms is the Viterbi algorithm, which returns the state path that has the
highest probability of generating a given sequence. Here, we explore an extension to
this algorithm that allows us to find the k paths of highest probabilities. The naive
implementation of k best Viterbi paths is highly space-inefficient, so we adapt recent
work on the Viterbi algorithm for a single path to this domain. Out algorithm uses
much less memory than the naive approach. We then investigate the usefulness
of the k best Viterbi paths on the example of transmembrane protein topology
prediction. For membrane proteins, even simple path combination algorithms give
good explanations, and if we look at the paths we are combining, we can give a
sense of confidence in the explanation as well. For proteins with two topologies,
the k best paths can give insight into both correct explanations of a sequence, a
feature lacking from traditional algorithms in this domain.
| Identifer | oai:union.ndltd.org:WATERLOO/oai:uwspace.uwaterloo.ca:10012/4603 |
| Date | 08 1900 |
| Creators | Golod, Daniil |
| Source Sets | University of Waterloo Electronic Theses Repository |
| Language | English |
| Detected Language | English |
| Type | Thesis or Dissertation |
Page generated in 0.0021 seconds