Machine learning applied to omics data

In this chapter we illustrate the use of some Machine Learning techniques in the context of omics data. More precisely, we review and evaluate the use of Random Forest and Penalized Multinomial Logistic Regression for integrative analysis of genomics and immunomics in pancreatic cancer. Furthermore, we propose the use of association rules with predictive purposes to overcome the low predictive power of the previously mentioned models. Finally, we apply the reviewed methods to a real data set from TCGA made …

analysis association cancer context cs.lg data genomics logistic regression machine machine learning machine learning techniques multinomial pancreatic cancer predictive q-bio.gn random regression review rules stat.ap