Data/hora: 05/06/2024 18:13:14

Biblioteca(s): 	Embrapa Agricultura Digital.	
Data corrente: 	08/01/2010	
Data da última atualização: 	15/01/2020	
Autoria: 	HIGA, R. H.; TOZZI, C. L.	
Afiliação: 	ROBERTO HIROSHI HIGA, CNPTIA; CLÉSIO LUIS TOZZI, DCA/FEEC/UNICAMP.	
Título: 	Using Markov random fields to predict protein interface region.	
Ano de publicação: 	2009	
Fonte/Imprenta: 	In: Encontro dos alunos e docentes do departamento de engenharia de computação e automação industrial, 2., 2009, Campinas. Anais... Campinas: UNICAMP, 2009.	
Páginas: 	p. 22-25.	
Idioma: 	Inglês	
Conteúdo: 	Protein interface region prediction is very helpful for molecular biologists since it is able to identify specific amino acids potentially inside the interface region for further experimental analysis. This can save specialists time as well as financial budget. Usually, this problem is modeled as a classification problem with contextual information taken into consideration in a post-processing procedure. We claim that this problem can be modeled as a contextual classification problem and propose to use Markov Random Fields - MRF for that. Obtained results show that the performance for this approach is compatible to other methods and deserves further research. Currently, we are evaluating this approach considering different parameters for the MRF models and establishing a suitable criterion for comparison with previous results.	
Palavras-Chave: 	Contextual classification; Interface proteica; Markov random fields; Modelagem; Pattern recognition; Protein interface region prediction.	
Categoria do assunto: 	X Pesquisa, Tecnologia e Engenharia	
Registro original: 	Embrapa Agricultura Digital (CNPTIA)	
Exemplares: 	CNPTIA - (14429-1) - UPC - RA - PP -  -  - 2010.00007 - DES
CNPTIA - (14429-2) - UPC - RA - DD -  -  -  - DIS