Leveraging Sequence Classiﬁcation by
Taxonomy-based Multitask Learning
Christian Widmer, Jose Leiva, Yasemin Altun and Gunnar Raetsch
In: RECOMB 2010(2009).
In this work we consider an inference task that biologists are very good
at: deciphering biological processes by bringing together knowledge
that has been obtained by experiments using various organisms,
while respecting the differences and commonalities of these organisms.
We look at this problem from an sequence analysis point of view, where we
aim at solving the same classification task in different organisms.
We investigate the challenge of combining information from related
organisms, whereas we consider the relation between the organisms
to be defined by a tree structure derived from their phylogeny.
Multitask learning, a machine learning technique that recently received
considerable attention, considers the problem of learning across tasks
that are related to each other.
We treat each organism as one task and
present three novel multitask learning methods to handle situations in
which the relationships among tasks can be described by a hierarchy.
These algorithms are designed for large-scale applications
and are therefore applicable to problems with a large number of training examples,
which are frequently encountered in sequence analysis.
We perform experimental analysis on synthetic data sets in order
to illustrate the properties of our algorithms.
Moreover, we consider a problem from genomic sequence
analysis, namely splice site recognition, to illustrate the usefulness
of our approach.
We show that intelligently combining data from $15$ eukaryotic
organisms can indeed significantly improve the prediction
performance compared to traditional learning approaches.
On a broader perspective, we expect that algorithms like the ones presented in this
work have the potential to complement and enrich the strategy of
homology-based sequence analysis that are currently the quasi-standard
in biological sequence analysis.