Machine learning applied to omics data
This work addresses predictive modeling for cancer immunology, but it is incremental as it reviews and applies existing methods to a specific dataset.
The authors tackled the problem of predicting immunological infiltration in pancreatic cancer using omics data, achieving good performance with methods like Random Forest and Penalized Multinomial Logistic Regression on a dataset of 107 tumoral samples and 117,486 SNPs.
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.