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

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 microarr...

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Bibliographic Details
Published in:International journal of medical informatics (Shannon, Ireland) Ireland), 2005-08, Vol.74 (7), p.491-503
Main Authors: Statnikov, Alexander, Tsamardinos, Ioannis, Dosbayev, Yerbolat, Aliferis, Constantin F.
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Biomarkers - analysis
Computer-assisted
Decision support systems
Diagnosis
Diagnosis, Computer-Assisted
Gene Expression
Gene expression microarray analysis
Humans
Models, Genetic
Neoplasms
Neoplasms - diagnosis
Neoplasms - genetics
Oligonucleotide Array Sequence Analysis - methods
Software
United States
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Summary: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.
ISSN:1386-5056
1872-8243
DOI:10.1016/j.ijmedinf.2005.05.002