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Hierarchical classification of transposable elements and protein functions making use of machine learning

Grant number: 16/25078-0
Support type:Scholarships in Brazil - Scientific Initiation
Effective date (Start): March 01, 2017
Effective date (End): June 30, 2018
Field of knowledge:Physical Sciences and Mathematics - Computer Science
Principal researcher:Ricardo Cerri
Grantee:Bruna Zamith Santos
Home Institution: Centro de Ciências Exatas e de Tecnologia (CCET). Universidade Federal de São Carlos (UFSCAR). São Carlos , SP, Brazil
Associated scholarship(s):17/13218-5 - Predicting protein functions via interaction prediction, BE.EP.IC

Abstract

Proteins are macromolecules responsible for almost every task necessary for the maintenance of cells, playing an essential role in the behavior and regulation of organisms. Advances in Molecular Biology have allowed an almost complete listing of the proteins that make up the organisms. However, there are a large number of proteins whose function is still unknown, opening space for a new research focus in Molecular Biology. In parallel, Transposable Elements (TEs) are DNA sequences that have the ability to change their location within the genome, affecting, thereby, the activity of certain genes. When TEs are inserted in other genes, they can alter or reduce the activity of certain proteins. Given the need for correct identification and classification of TEs and protein functions, there are several techniques currently employed to achieve this goal. The main ones involve a lot of manual work or they are very specific, restricting the set of data to be classified. Moreover, the classes involved in these problems are structured hierarchically, a fact often ignored by the proposed techniques. Thus, this project proposes the use of Machine Learning (ML) for the hierarchical classification of TEs and functions of proteins. In addition to the use of several methods already known, a new contribution will be the investigation of different ways of using positive and negative examples during the induction of hierarchical methods. This is especially important in methods that follow the top-down strategy. All methods will be evaluated using specific measures for hierarchical problems. (AU)

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