论文信息 - Development of a literature informed Bayesian machine learning method for feature extraction and classification

Development of a literature informed Bayesian machine learning method for feature extraction and classification

Background Gene expression profiling is a powerful approach to identify markers for classification of samples; however, it has major limitations that hinder performance. Typically, a large number of variables are assessed compared to relatively small sample sizes. In addition, it is difficult to identify biologically informative markers which have high predictive power [1-3]. Thus, the goal of this study was to develop a machine learning approach that is able to bridge classification accuracy and biological function.

Behrouz Madahian | Ramin Homayouni | Lih Yuan Deng

[1] Behrouz Madahian,et al. Application of Sparse Bayesian Generalized Linear Model to Gene Expression Data for Classification of Prostate Cancer Subtypes , 2014 .

[2] Putri W. Novianti,et al. Evaluation of Gene Expression Classification Studies: Factors Associated with Classification Performance , 2014, PloS one.

[3] J. Mesirov,et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.

[4] A. Dupuy,et al. Critical review of published microarray studies for cancer outcome and guidelines on statistical analysis and reporting. , 2007, Journal of the National Cancer Institute.