GEMS: a system for automated cancer diagnosis and biomarker discovery from microarray gene expression data.

Abstract:

:The success of treatment of patients with cancer depends on establishing an accurate diagnosis. To this end, we have built a system called GEMS (gene expression model selector) for the automated development and evaluation of high-quality cancer diagnostic models and biomarker discovery from microarray gene expression data. In order to determine and equip the system with the best performing diagnostic methodologies in this domain, we first conducted a comprehensive evaluation of classification algorithms using 11 cancer microarray datasets. In this paper we present a preliminary evaluation of the system with five new datasets. The performance of the models produced automatically by GEMS is comparable or better than the results obtained by human analysts. Additionally, we performed a cross-dataset evaluation of the system. This involved using a dataset to build a diagnostic model and to estimate its future performance, then applying this model and evaluating its performance on a different dataset. We found that models produced by GEMS indeed perform well in independent samples and, furthermore, the cross-validation performance estimates output by the system approximate well the error obtained by the independent validation. GEMS is freely available for download for non-commercial use from http://www.gems-system.org.

journal_name

Int J Med Inform

authors

Statnikov A,Tsamardinos I,Dosbayev Y,Aliferis CF

doi

10.1016/j.ijmedinf.2005.05.002

subject

Has Abstract

pub_date

2005-08-01 00:00:00

pages

491-503

issue

7-8

eissn

1386-5056

issn

1872-8243

pii

S1386-5056(05)00052-3

journal_volume

74

pub_type

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