Hidden Markov model and Chapman Kolmogrov for protein structures prediction from images.

Kamal, MS; Chowdhury, L; Khan, MI; Ashour, AS; Tavares, JMRS; Dey, N

Hidden Markov model and Chapman Kolmogrov for protein structures prediction from images.

Kamal, MS Chowdhury, L Khan, MI Ashour, AS Tavares, JMRS Dey, N

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Publisher:: Elsevier BV
Publication Type:: Journal Article
Citation:: Comput Biol Chem, 2017, 68, pp. 231-244
Issue Date:: 2017-06

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Field	Value	Language
dc.contributor.author	Kamal, MS
dc.contributor.author	Chowdhury, L
dc.contributor.author	Khan, MI
dc.contributor.author	Ashour, AS
dc.contributor.author	Tavares, JMRS
dc.contributor.author	Dey, N
dc.date.accessioned	2023-01-03T04:11:54Z
dc.date.available	2017-04-11
dc.date.available	2023-01-03T04:11:54Z
dc.date.issued	2017-06
dc.identifier.citation	Comput Biol Chem, 2017, 68, pp. 231-244
dc.identifier.issn	1476-9271
dc.identifier.issn	1476-928X
dc.identifier.uri	http://hdl.handle.net/10453/164641
dc.description.abstract	Protein structure prediction and analysis are more significant for living organs to perfect asses the living organ 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 compared to the NN. It employs statistical data analysis to enhance the prediction accuracy. The current work proposed a protein prediction approach from protein images based on Hidden Markov Model and Chapman Kolmogrov equation. Initially, a preprocessing stage was applied for protein images' binarization 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 protein structures. Finally, Hidden Markov model and Chapman Kolmogrov equation are applied on the classified structures for predicting the protein structure. The execution time and algorithmic performances are measured to evaluate the primary, secondary and tertiary protein structure prediction.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	Elsevier BV
dc.relation.ispartof	Comput Biol Chem
dc.relation.isbasedon	10.1016/j.compbiolchem.2017.04.003
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	03 Chemical Sciences, 06 Biological Sciences, 08 Information and Computing Sciences
dc.subject.classification	Bioinformatics
dc.subject.mesh	Algorithms
dc.subject.mesh	Markov Chains
dc.subject.mesh	Neural Networks, Computer
dc.subject.mesh	Protein Conformation
dc.subject.mesh	Proteins
dc.subject.mesh	Proteins
dc.subject.mesh	Markov Chains
dc.subject.mesh	Protein Conformation
dc.subject.mesh	Algorithms
dc.subject.mesh	Neural Networks, Computer
dc.title	Hidden Markov model and Chapman Kolmogrov for protein structures prediction from images.
dc.type	Journal Article
utslib.citation.volume	68
utslib.location.activity	England
utslib.for	03 Chemical Sciences
utslib.for	06 Biological Sciences
utslib.for	08 Information and Computing Sciences
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2023-01-03T04:11:52Z
pubs.publication-status	Published
pubs.volume	68

Abstract:

Protein structure prediction and analysis are more significant for living organs to perfect asses the living organ 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 compared to the NN. It employs statistical data analysis to enhance the prediction accuracy. The current work proposed a protein prediction approach from protein images based on Hidden Markov Model and Chapman Kolmogrov equation. Initially, a preprocessing stage was applied for protein images' binarization 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 protein structures. Finally, Hidden Markov model and Chapman Kolmogrov equation are applied on the classified structures for predicting the protein structure. The execution time and algorithmic performances are measured to evaluate the primary, secondary and tertiary protein structure prediction.

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http://hdl.handle.net/10453/164641