When: Tuesday, 15th March, 10am
Where: 78-420 (Rm420, GPSouth Building)

Speaker: John Hawkins

Title: Recurrent Neural Network Architectures for Predicting the Fates
of Proteins

Abstract:

The selection of machine learning techniques requires a certain
sensitivity to the requirements of the problem. In particular the
problem can be made more tractable by deliberately using algorithms that
are biased towards solutions of the requisite kind. The central
hypothesis of this thesis will be that recurrent architectures have a
natural bias towards the general problem domain of which biological
sequence tasks are a subset. The central goal of the project will be to
gather evidence for this hypothesis by applying recurrent networks to
problems of classifying protein fates. A case study performed on the
prediction of protein subcellular localisation indicated that not only
are recurrent networks suitable to the task, but that as the patterns
within the sequence become more ambiguous, the choice of specific
recurrent architecture becomes more critical. Thus a subsidiary
hypothesis emerged that by refining the recurrent architecture it is
possible to tune it to the specific demands of the problem. Recurrent
neural networks have the added benefit that they are amenable to a
number of post training analysis techniques, particularly finite state
automata extraction and dynamical analysis of state node activations.
These techniques allow for a greater insight into both the nature
problem and the manner in which it was solved than most machine learning
approaches provide.
Thus, the final objective of the project will be to use the successful
classifiers to perform an analysis of the relationship between the
machines and the problem, to draw out insights into the corresponding
biological processes