Name
Abstract | ||
|---|---|---|
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 several 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 analyses 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. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-12683-3_34 | RECOMB |
Keywords | Field | DocType |
traditional learning approach,leveraging sequence classification,classification task,inference task,sequence analysis point,biological sequence analysis,taxonomy-based multitask learning,sequence analysis,experimental analysis,phylogeny multitask learning,homology-based sequence analysis,genomic sequence analysis,synthetic data,tree structure,machine learning,genome sequence,biological process | Hinge loss,Multi-task learning,Inference,Computer science,Support vector machine,Tree structure,Artificial intelligence,Bioinformatics,String kernel,Hierarchy,Machine learning,Organism | Conference |
Volume | ISSN | ISBN |
6044 | 0302-9743 | 3-642-12682-0 |
Citations | PageRank | References |
18 | 0.75 | 10 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christian Widmer | 1 | 239 | 27.29 |
| Jose Leiva | 2 | 18 | 0.75 |
| yasemin altun | 3 | 2463 | 150.46 |
| Gunnar Rätsch | 4 | 5625 | 671.20 |