Name
Abstract | ||
|---|---|---|
This paper develops an alternative method for gene selection that combines model based clustering and binary classification. By averaging the covariates within the clusters obtained from model based clustering, we define "meta-covariates" and use them to build a probit regression model, thereby selecting clusters of similarly behaving genes, aiding interpretation. This simultaneous learning task is accomplished by an EM algorithm that optimises a single likelihood function which rewards good performance at both classification and clustering. We explore the performance of our methodology on a well known leukaemia dataset and use the Gene Ontology to interpret our results. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-04031-3_14 | PRIB |
Keywords | Field | DocType |
simultaneous learning task,gene ontology,inferring meta-covariates,good performance,single likelihood function,gene selection,alternative method,binary classification,em algorithm,leukaemia dataset,probit regression model,clustering,classification,regression model,likelihood function | Data mining,Binary classification,Computer science,Artificial intelligence,Cluster analysis,Gene selection,Covariate,Probit model,Likelihood function,Correlation clustering,Expectation–maximization algorithm,Bioinformatics,Machine learning | Conference |
Volume | ISSN | Citations |
5780 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 5 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Keith Harris | 1 | 14 | 1.60 |
| Lisa Mcmillan | 2 | 0 | 0.34 |
| Mark Girolami | 3 | 1382 | 141.16 |