Integrative mixture of experts to combine clinical factors and gene markers.
BACKGROUND
Microarrays are being increasingly used in cancer research to better characterize and classify tumors by selecting marker genes. However, as very few of these genes have been validated as predictive biomarkers so far, it is mostly conventional clinical and pathological factors that are being used as prognostic indicators of clinical course. Combining clinical data with gene expression data may add valuable information, but it is a challenging task due to their categorical versus continuous characteristics. We have further developed the mixture of experts (ME) methodology, a promising approach to tackle complex non-linear problems. Several variants are proposed in integrative ME as well as the inclusion of various gene selection methods to select a hybrid signature.


RESULTS
We show on three cancer studies that prediction accuracy can be improved when combining both types of variables. Furthermore, the selected genes were found to be of high relevance and can be considered as potential biomarkers for the prognostic selection of cancer therapy.


BACKGROUND
Integrative ME is implemented in the R package integrativeME (http://cran.r-project.org/).
Lê Cao
Kim-Anh
KA
ARC Centre of Excellence in Bioinformatics, Institute for Molecular Bioscience, University of Queensland, St Lucia, QLD, Australia. k.lecao@uq.edu.au
Meugnier
Emmanuelle
E
McLachlan
Geoffrey J
GJ
D000465:Algorithms; D001499:Bayes Theorem; D014408:Biomarkers, Tumor / Q000378:metabolism*; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling; D005819:Genetic Markers; D006801:Humans; D008297:Male; D008495:Medical Oncology / Q000379:methods*; D008954:Models, Biological; D008957:Models, Genetic; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis; D011471:Prostatic Neoplasms / Q000378:metabolism; D015203:Reproducibility of Results
D014408:Biomarkers, Tumor; D005819:Genetic Markers