Machine learning applied to omics data
Aida Calviño Almudena Moreno-Ribera Silvia Pineda
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 of 107 tumoral pancreatic
samples and 117,486 germline SNPs, showing the good performance of the proposed
methods to predict the immunological infiltration in pancreatic cancer.