Meta-learning use algorithm recommendation for gene expression data analysis

• Grant number: 17/05672-8
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: May 01, 2017
• End date: December 31, 2017
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Principal Investigator: André Carlos Ponce de Leon Ferreira de Carvalho
• Grantee: Edesio Pinto de Souza Alcobaça Neto
• Host Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil

Abstract

Cancer is one of the leading causes of death today. The understanding of its internal mechanisms and the design of computational models capable of improving its diagnosis will have strong benefits. New sequencing technologies, based on RNA-Seq, have made available a large amount of data, which can be used to diagnose cancer. However, each machine learning algorithm has an inductive bias that makes best suited to a particular subset of problems. This project studies the use of strategies that improve or optimize the selection of classification algorithms for machine learning in the context of data classification. We will investigate the potential of using meta-learning to associate features present in a data set with the most appropriate classification techniques to deal with them in the task of identifying tumors through gene expression using RNA-Seq and Microarray technology. (AU)