Hepatocellular Carcinoma tumor stage classification and gene selection using machine learning models.

  • Martin Palazzo Universite de Technologie de Troyes - Instituto de Investigacion en Biomedicina de Buenos Aires - Universidad Tecnologica Nacional Facultad Regional Buenos Aires
  • Pierre Beauseroy Institut Charles Delaunay, Universite de Technologie de Troyes
  • Patricio Yankilevich Instituto de Investigacion en Biomedicina de Buenos Aires

Resumen

Cancer researchers are facing the opportunity to analyze and learn from big quantities of omic profiles of tumor samples. Different omic data is now available in several databases and the bioinformatics data analysis and interpretation are current bottlenecks. In this study somatic mutations and gene expression data from Hepatocellular carcinoma tumor samples are used to discriminate by Kernel Learning between tumor subtypes and early and late stages. This classification will allow medical doctors to establish an appropriate treatment according to the tumor stage. By building kernel machines we could discriminate both classes with an acceptable classification accuracy. Feature selection have been implemented to select the key genes which differential expression improves the separability between the samples of early and late stages.

Publicado
2019-03-29
Cómo citar
Palazzo, M., Beauseroy, P., & Yankilevich, P. (2019). Hepatocellular Carcinoma tumor stage classification and gene selection using machine learning models. Electronic Journal of SADIO (EJS), 18(1), 26-42. Recuperado a partir de https://ojs.sadio.org.ar/index.php/EJS/article/view/83