Artificial Intelligence-Driven Prediction Revealed CFTR Associated with Therapy Outcome of Breast Cancer: A Feasibility Study.

Journal: Oncology
Published Date: Jul 18, 2024

Abstract

INTRODUCTION: In silico tools capable of predicting the functional consequences of genomic differences between individuals, many of which are AI-driven, have been the most effective over the past two decades for non-synonymous single nucleotide variants (nsSNVs). When appropriately selected for the purpose of the study, a high predictive performance can be expected. In this feasibility study, we investigate the distribution of nsSNVs with an allele frequency below 5%. To classify the putative functional consequence, a tier-based filtration led by AI-driven predictors and scoring system was implemented to the overall decision-making process, resulting in a list of prioritised genes.

Authors

  • Mária Kováčová
    Third Faculty of Medicine, Charles University, Prague, Czechia.
  • Viktor Hlaváč
    Laboratory of Pharmacogenomics, Biomedical Center, Faculty of Medicine in Pilsen, Charles University, Pilsen, Czechia.
  • Renata Koževnikovová
    Department of Oncosurgery, MEDICON, Prague, Czechia.
  • Karel Rauš
    Institute for the Care for Mother and Child, Prague, Czechia.
  • Jiří Gatěk
    Department of Surgery, EUC Hospital and University of Tomas Bata in Zlin, Zlin, Czechia.
  • Pavel Souček
    Institute of Complex Systems, South Bohemian Research Centre of Aquaculture and Biodiversity of Hydrocenoses, Faculty of Fisheries and Protection of Waters, University of South Bohemia in České Budějovice, Zámek 136, Nové Hrady 37 333, Czech Republic. psoucek@frov.jcu.cz.

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