Machine Learning Applied to Omics Data

  1. Calviño Martínez, Aida 1
  2. Moreno-Ribera, Almudena
  3. Pineda Sanjuan, Silvia
  1. 1 Universidad Complutense de Madrid
    info

    Universidad Complutense de Madrid

    Madrid, España

    ROR 02p0gd045

Livre:
Statistical Methods at the Forefront of Biomedical Advances

ISBN: 9783031327285,  9783031327292

Année de publication: 2023

Pages: 21-43

Type: Chapitre d'ouvrage

DOI: 10.1007/978-3-031-32729-2 GOOGLE SCHOLAR lock_openAccès ouvert editor

Résumé

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.