Molecular docking and machine learning analysis of Abemaciclib in colon cancer.

ABSTRACT:

Background

The main challenge in cancer research is the identification of different omic variables that present a prognostic value and personalised diagnosis for each tumour. The fact that the diagnosis is personalised opens the doors to the design and discovery of new specific treatments for each patient. In this context, this work offers new ways to reuse existing databases and work to create added value in research. Three published signatures with significante prognostic value in Colon Adenocarcinoma (COAD) were indentified. These signatures were combined in a new meta-signature and validated with main Machine Learning (ML) and conventional statistical techniques. In addition, a drug repurposing experiment was carried out through Molecular Docking (MD) methodology in order to id

SUBMITTER: Linares-Blanco J 

PROVIDER: S-EPMC7346626 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature