Applying Inspired Algorithms for Nature to Classification of depolymerase Enzyme Used in Biofuel Production

• Grant number: 15/06780-3
• Support Opportunities: Scholarships in Brazil - Master
• Start date: June 01, 2015
• End date: February 29, 2016
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Principal Investigator: Fabricio Aparecido Breve
• Grantee: Diego Henrique Negretto
• Host Institution: Instituto de Geociências e Ciências Exatas (IGCE). Universidade Estadual Paulista (UNESP). Campus de Rio Claro. Rio Claro , SP, Brazil
• Associated research grant: 11/17396-9 - Machine learning using models inspired by nature, AP.JP

Abstract

The production, exploitation and use of certain liquid fuels, such as petroleum products, has led to a depletion of non-renewable resources and may cause serious environmental problems. Thus, the search for ways to produce biofuels through abundant and renewable sources, such as the depolymerases enzymes present in microorganisms, is of great importance. Research in this field, generates massive amount of biological data, and consequently, the data analysis becomes increasingly non-trivial, thus requiring the use of computer solutions. The use of machine learning, allows classification of data from an automatic way and is useful in situations where domain experts are able to accomplish this task, but can not describe the heuristics used. Thus, it is intended with this project, apply algorithms inspired by nature for the classification of data on enzymes des polymerase related to biofuel production in their respective enzyme complexes.

Academic Publications

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

NEGRETTO, Diego Henrique. Semi-supervised machine learning algorithms based on graph applied in bioinformatics. 2016. Master's Dissertation - Universidade Estadual Paulista (Unesp). Instituto de Biociências Letras e Ciências Exatas. São José do Rio Preto São José do Rio Preto.