Leveraging Sequence Classification by Taxonomy-Based Multitask Learning

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 fo...

• Type: Conference
• Year: 2010
• Where: RECOMB
• Authors: Christian Widmer, Jose Leiva, Yasemin Altun, Gunnar Rätsch
• Keywords: Computational Biology, Multitask Learning Methods, RECOMB 2010, Sequence Analysis, Sequence Analysis Point