Hierarchical Multi-classification with Predictive Clustering Trees in Functional Genomics


Autoria(s): Clare, Amanda; D?eroski, Sa?o; Struyf, Jan; Blockeel, Hendrik
Contribuinte(s)

Department of Computer Science

Bioinformatics and Computational Biology Group

Data(s)

25/04/2006

25/04/2006

2005

Resumo

Struyf, J., Dzeroski, S. Blockeel, H. and Clare, A. (2005) Hierarchical Multi-classification with Predictive Clustering Trees in Functional Genomics. In proceedings of the EPIA 2005 CMB Workshop

This paper investigates how predictive clustering trees can be used to predict gene function in the genome of the yeast Saccharomyces cerevisiae. We consider the MIPS FunCat classification scheme, in which each gene is annotated with one or more classes selected from a given functional class hierarchy. This setting presents two important challenges to machine learning: (1) each instance is labeled with a set of classes instead of just one class, and (2) the classes are structured in a hierarchy; ideally the learning algorithm should also take this hierarchical information into account. Predictive clustering trees generalize decision trees and can be applied to a wide range of prediction tasks by plugging in a suitable distance metric. We define an appropriate distance metric for hierarchical multi-classification and present experiments evaluating this approach on a number of data sets that are available for yeast.

Non peer reviewed

Identificador

Clare , A , D?eroski , S , Struyf , J & Blockeel , H 2005 , ' Hierarchical Multi-classification with Predictive Clustering Trees in Functional Genomics ' .

PURE: 68118

PURE UUID: ab77444b-02af-44e9-8fa8-739efd6063f6

dspace: 2160/131

http://hdl.handle.net/2160/131

Idioma(s)

eng

Tipo

/dk/atira/pure/researchoutput/researchoutputtypes/contributiontoconference/paper

Direitos