Pipeline design to identify key features in prognosis biomarker analysis using a real lung cancer dataset

@inproceedings{Valds2017PipelineDT,
  title={Pipeline design to identify key features in prognosis biomarker analysis using a real lung cancer dataset},
  author={Maŕıa Gabriela Vald{\'e}s and Xavier Rafael-Palou and Ivan Galv{\'a}n-Femeńıa and X Romero Dur{\'a}n and Jun Yokota and Ricard Gavald{\`a} and Rafael de Cid and Vicent J. Ribas Ripoll},
  year={2017}
}
During the last decade, the interest to apply machine learning algorithms to genomic data has significantly increased for a variety of bioinformatics applications. Analyzing this type of data entails tackling difficulties related to high-dimensionality and class imbalance for knowledge extraction and identifying important features. In this study, we propose a general framework to tackle those challenges by stacking different machine learning algorithms and techniques to choose the best… CONTINUE READING