RT Book, Section
T1 Machine learning applied to omics data
A1 Calviño Martínez, Aída
A1 Moreno Ribera, Almudena
A1 Pineda Sanjuan, Silvia
A2 Larriba, Yolanda
AB 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.
PB Springer
SN 978-3-031-32728-5
SN 978-3-031-32729-2
YR 2023
FD 2023
LK https://hdl.handle.net/20.500.14352/91677
UL https://hdl.handle.net/20.500.14352/91677
LA eng
NO Calviño, A., Moreno-Ribera, A., Pineda, S., & Larriba, Yolanda. (2023). Statistical methods at the forefront of biomedical advances. In Machine learning applied to omics data (pp. 21–43).
NO AstraZeneca
DS Docta Complutense
RD 10 abr 2025