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

Journal: BMC molecular and cell biology
PMID: 32640984

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 identify new potential treatments in COAD.

Authors

  • Jose Liñares-Blanco
    Department of Computer Science and Information Technologies, Faculty of Computer Science, University of A Coruña, CITIC, Campus Elviña s/n, A Coruña, 15071, Spain.
  • Cristian R Munteanu
    Department of Information and Communication Technologies, Computer Science Faculty, University of A Coruna, Campus de Elviña s/n, 15071, A Coruña, Spain, phone/fax: +34-981167000/+34-981167160. crm.publish@gmail.com.
  • Alejandro Pazos
    Department of Computer Science and Information Technologies, Faculty of Computer Science, CITIC-Research Center of Information and Communication Technologies, Universidade da Coruña, A Coruña, Spain.
  • Carlos Fernandez-Lozano
    Department of Computer Science and Information Technologies, Faculty of Computer Science, CITIC-Research Center of Information and Communication Technologies, Universidade da Coruña, A Coruña, Spain.

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