A Comparative Analysis of Gene Expression Profiling by Statistical and Machine Learning Approaches

Many machine learning models have been proposed to classify phenotypes from gene expression data. In addition to their good performance, these models can potentially provide some understanding of phenotypes by extracting explanations for their decisions. These explanations often take the form of a list of genes ranked in order of importance for the predictions, the highest-ranked genes being interpreted as linked to the phenotype. We discuss the biological and the methodological limitations of such explanations. Experiments are performed on several …

analysis comparative analysis cs.lg data decisions form gene genes good list machine machine learning machine learning models performance profiling q-bio.gn statistical understanding