1. Repositório Institucional da UFMG
2. Trabalhos Acadêmicos
3. Dissertações e Teses
4. Pós-Graduação em Ciência da Computação
5. Dissertações de Mestrado

Tipo:	Dissertação
Título:	Gaming and Data Mining in the Learning and Evaluation of Contacts in Biological Complexes
Título(s) alternativo(s):	O Uso de Jogos e Mineração de Dados no Aprendizado e Avaliação de Contatos em Complexos Biológicos
Autor(es):	Marcos Felipe Martins Silva
Primeiro Orientador:	Raquel Cardoso de Melo Minardi
Primeiro Coorientador:	Cristiane Neri Nobre
metadata.dc.contributor.advisor-co2:	http://lattes.cnpq.br/6608329267627163
Primeiro membro da banca :	Gisele Lobo Pappa
Segundo membro da banca:	Lucas Bleicher
Resumo:	Molecules of life (carbohydrates, lipids, nucleic acids, and proteins) can change their conformation in the space frequently due to non-covalent bonds (or contacts) between their molecules. The comprehension of patterns created through the contacts of atoms and amino acids has been used as an aid in solving a range of problems in bioinformatics such as protein conformation, the functional similarities between proteins, structural alignment, thermodynamic stability prediction, prediction of protein structures, drug design, and so forth. Several paradigms of calculation of contacts have been developed. However, all models present advantages and disadvantages. The model based only on distance present the problem of not guaranteeing that just the first layer of neighbor atoms are connected by edges, and occlusions may occur (when there is an intervening atom in-between). Concerning geometry, some techniques determine contacts in proteins through Delaunay and Voronoi tesselations. These methods present an issue where two atoms could establish an interaction, but the unique triangulation did not identify them as connected. Besides, the state-of-the-art lacks visualization techniques of non-covalent interactions, what would highly help in the identification of occlusions. An efficient manner to make the visualization of non-covalent interaction is through the use of games due to its benefits such as pleasure, stimulation, creativity, and enthusiasm. Also, several games in education have been an aid of understanding open problems in biochemistry. Hence, the present dissertation proposes a methodology for the construction and evaluation of a manually curated database for the classification of contacts between atoms of amino acids in protein chains through the use of digital games. To evaluate the built database with the state-of-the-art, data mining methods were used to find patterns in the classification of contacts. We also counted on the participation of inexperienced players in biochemistry, and we have significant evidence that they learned about molecular non-covalent bonds as they played.
Idioma:	eng
País:	Brasil
Editor:	Universidade Federal de Minas Gerais
Sigla da Instituição:	UFMG
Departamento:	ICX - DEPARTAMENTO DE CIÊNCIA DA COMPUTAÇÃO
Curso:	Programa de Pós-Graduação em Ciência da Computação
Tipo de Acesso:	Acesso Aberto Atribuição-SemDerivados 3.0 Portugal
metadata.dc.rights.uri:	http://creativecommons.org/licenses/by-nd/3.0/pt/
URI:	http://hdl.handle.net/1843/31231
Data do documento:	23-Jan-2018
Aparece nas coleções:	Dissertações de Mestrado

Este item está licenciada sob uma Licença Creative Commons