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Microarray Gene Expression Data Integration: An Application to Brain Tumor Grade Determination

  • Eduardo ValenteEmail author
  • Miguel Rocha
Conference paper
  • 602 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 375)

Abstract

World Health Organization ranks brain tumors in four stages, being the fourth grade the most aggressive. Glioblastoma, a fourth grade tumor, is one of the most severe human diseases that almost inevitability leads to death. Physicians address the classification in grades through direct inspection. Indeed, there is a need for good automatic predictors of tumor grade, which are not affected by human misclassification errors and that can be made with less invasive diagnostic tools. This work address the stages involved in the process of selecting a good tumor grade predictor, based on microarray gene expression data. In this work, the information integration from heterogeneous platforms is highlighted, evidencing the particularities of choosing approaches working at gene, transcript or probeset levels. Distinct machine learning algorithms and integration methods are tested, analyzing their ability to produce a good set of predictors for tumor grade.

Keywords

Gene expression Glioblastoma Microarrays data integration Filter methods Wrapper methods 

Notes

Acknowledgments

The work is partially funded by Project 23060, PEM - Technological Support Platform for Metabolic Engineering, co- funded by FEDER through Portuguese QREN under the scope of the Technological Research and Development Incentive system, North Operational.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Computer Science and Information SystemsIPCBCastelo BrancoPortugal
  2. 2.Centre of Biological EngineeringUniversity of MinhoBragaPortugal

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