Development of machine learning techniques and artificial intelligence for the study of cancer related cachexia

• Grant number: 17/17096-1
• Support Opportunities: Scholarships in Brazil - Doctorate
• Start date: December 01, 2017
• End date: August 31, 2021
• Field of knowledge: Interdisciplinary Subjects - Physics - Condensed Matter Physics
• Principal Investigator: Alexandre Alarcon do Passo Suaide
• Grantee: Natasha Fioretto Aguero
• Host Institution: Instituto de Física (IF). Universidade de São Paulo (USP). São Paulo , SP, Brazil

Abstract

Cancer patients may develop a syndrome called cachexia, in which there is a great loss of skeletal muscle mass and it is linked to a pessimistic prognosis. One hypothesis is that epigenetic factors, such as physical conditioning, could influence the evolution of this syndrome. The cancer metabolism group of the Institute of Biomedical Sciences of USP, headed by Prof. Dr. Marília Seelaender, has performed several experiments to test this assertion, from clinical examinations to DNA sequencing of human patients. The massive amount of the obtained data makes it necessary to apply computational methods for statistical analysis. In this project, machine learning techniques will be implemented in order to identify markers that may aid in the early diagnosis of cachexia in cancer patients, in addition to identifying possible epigenetic factors that may influence the prognosis of the disease.

Academic Publications

(References retrieved automatically from State of São Paulo Research Institutions)

AGUERO, Natasha Fioretto. Development of machine learning and artificial intelligence techniques in the study of cancer-related cachexia. 2023. Doctoral Thesis - Universidade de São Paulo (USP). Instituto de Física (IF/SBI) São Paulo.