Learning of transcriptional regulatory interactions using microarray data is an important and challenging problem in bioinformatics. Several solutions to this problem have been proposed both in a model based statistical approach and in an unsupervised machine learning approach. In a model based approach a very popular choice are Gaussian graphical models where it is assumed that microarray data form an i.i.d. multivariate normal sample. In this framework, Castelo and Roverato (2006) introduced a quantity that they called the non-rejection rate that can be used to learn Gaussian graphical models when the sample size is smaller than the number of variables. Here we present an application of the non-rejection rate, in an unsupervised learning approach, to a compendium of data from different microarray experiments and shown that it provides competitive performance with respect to other widely used methods.

An application of the non-rejection rate in meta-analysis

A. Roverato;
2009

Abstract

Learning of transcriptional regulatory interactions using microarray data is an important and challenging problem in bioinformatics. Several solutions to this problem have been proposed both in a model based statistical approach and in an unsupervised machine learning approach. In a model based approach a very popular choice are Gaussian graphical models where it is assumed that microarray data form an i.i.d. multivariate normal sample. In this framework, Castelo and Roverato (2006) introduced a quantity that they called the non-rejection rate that can be used to learn Gaussian graphical models when the sample size is smaller than the number of variables. Here we present an application of the non-rejection rate, in an unsupervised learning approach, to a compendium of data from different microarray experiments and shown that it provides competitive performance with respect to other widely used methods.
2009
Statistical methods for the analysis of large data-sets
Riunione intermedia della Società Italiana di Statistica - Statistical Methods for the snalysis of large data-sets
9788861294257
File in questo prodotto:
Non ci sono file associati a questo prodotto.
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3280869
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
  • OpenAlex ND
social impact