Please use this identifier to cite or link to this item: http://hdl.handle.net/10216/103549
Author(s): Md. Sarwar Kamal
Linkon Chowdhury
Mohammad Ibrahim Khan
Amira S. Ashour
João Manuel R. S. Tavares
Nilanjan Dey
Title: Hidden Markov model and Chapman Kolmogrov for protein structures prediction from images
Issue Date: 2017-06-01
Abstract: Protein structure prediction and analysis are more significant for living organs to perfect asses the livingorgan functionalities. Several protein structure prediction methods use neural network (NN). However,the Hidden Markov model is more interpretable and effective for more biological data analysis comparedto the NN. It employs statistical data analysis to enhance the prediction accuracy. The current workproposed a protein prediction approach from protein images based on Hidden Markov Model andChapman Kolmogrov equation. Initially, a preprocessing stage was applied for protein imagesbinarization using Otsu technique in order to convert the protein image into binary matrix. Subsequently,two counting algorithms, namely the Flood fill and Warshall are employed to classify the proteinstructures. Finally, Hidden Markov model and Chapman Kolmogrov equation are applied on the classifiedstructures for predicting the protein structure. The execution time and algorithmic performances aremeasured to evaluate the primary, secondary and tertiary protein structure prediction.
Subject: Ciências Tecnológicas, Ciências médicas e da saúde
Technological sciences, Medical and Health sciences
URI: http://hdl.handle.net/10216/103549
Document Type: Artigo em Revista Científica Internacional
Rights: openAccess
Appears in Collections:FEUP - Artigo em Revista Científica Internacional

Files in This Item:
File Description SizeFormat 
187481.pdfPaper Draft3.54 MBAdobe PDFThumbnail
View/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.