Exploring function prediction in protein interaction networks via clustering methods.

Complex networks have recently become the focus of research in many fields. Their structure reveals crucial information for the nodes, how they connect and share information. In our work we analyze protein interaction networks as complex networks for their functional modular structure and later use...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Kire Trivodaliev, Aleksandra Bogojeska, Ljupco Kocarev
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/599d5c4643da4f15a727ead631e4ed6c
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:599d5c4643da4f15a727ead631e4ed6c
record_format	dspace
spelling	oai:doaj.org-article:599d5c4643da4f15a727ead631e4ed6c2021-11-11T08:21:03ZExploring function prediction in protein interaction networks via clustering methods.1932-620310.1371/journal.pone.0099755https://doaj.org/article/599d5c4643da4f15a727ead631e4ed6c2014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24972109/?tool=EBIhttps://doaj.org/toc/1932-6203Complex networks have recently become the focus of research in many fields. Their structure reveals crucial information for the nodes, how they connect and share information. In our work we analyze protein interaction networks as complex networks for their functional modular structure and later use that information in the functional annotation of proteins within the network. We propose several graph representations for the protein interaction network, each having different level of complexity and inclusion of the annotation information within the graph. We aim to explore what the benefits and the drawbacks of these proposed graphs are, when they are used in the function prediction process via clustering methods. For making this cluster based prediction, we adopt well established approaches for cluster detection in complex networks using most recent representative algorithms that have been proven as efficient in the task at hand. The experiments are performed using a purified and reliable Saccharomyces cerevisiae protein interaction network, which is then used to generate the different graph representations. Each of the graph representations is later analysed in combination with each of the clustering algorithms, which have been possibly modified and implemented to fit the specific graph. We evaluate results in regards of biological validity and function prediction performance. Our results indicate that the novel ways of presenting the complex graph improve the prediction process, although the computational complexity should be taken into account when deciding on a particular approach.Kire TrivodalievAleksandra BogojeskaLjupco KocarevPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 6, p e99755 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Kire Trivodaliev Aleksandra Bogojeska Ljupco Kocarev Exploring function prediction in protein interaction networks via clustering methods.
description	Complex networks have recently become the focus of research in many fields. Their structure reveals crucial information for the nodes, how they connect and share information. In our work we analyze protein interaction networks as complex networks for their functional modular structure and later use that information in the functional annotation of proteins within the network. We propose several graph representations for the protein interaction network, each having different level of complexity and inclusion of the annotation information within the graph. We aim to explore what the benefits and the drawbacks of these proposed graphs are, when they are used in the function prediction process via clustering methods. For making this cluster based prediction, we adopt well established approaches for cluster detection in complex networks using most recent representative algorithms that have been proven as efficient in the task at hand. The experiments are performed using a purified and reliable Saccharomyces cerevisiae protein interaction network, which is then used to generate the different graph representations. Each of the graph representations is later analysed in combination with each of the clustering algorithms, which have been possibly modified and implemented to fit the specific graph. We evaluate results in regards of biological validity and function prediction performance. Our results indicate that the novel ways of presenting the complex graph improve the prediction process, although the computational complexity should be taken into account when deciding on a particular approach.
format	article
author	Kire Trivodaliev Aleksandra Bogojeska Ljupco Kocarev
author_facet	Kire Trivodaliev Aleksandra Bogojeska Ljupco Kocarev
author_sort	Kire Trivodaliev
title	Exploring function prediction in protein interaction networks via clustering methods.
title_short	Exploring function prediction in protein interaction networks via clustering methods.
title_full	Exploring function prediction in protein interaction networks via clustering methods.
title_fullStr	Exploring function prediction in protein interaction networks via clustering methods.
title_full_unstemmed	Exploring function prediction in protein interaction networks via clustering methods.
title_sort	exploring function prediction in protein interaction networks via clustering methods.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/599d5c4643da4f15a727ead631e4ed6c
work_keys_str_mv	AT kiretrivodaliev exploringfunctionpredictioninproteininteractionnetworksviaclusteringmethods AT aleksandrabogojeska exploringfunctionpredictioninproteininteractionnetworksviaclusteringmethods AT ljupcokocarev exploringfunctionpredictioninproteininteractionnetworksviaclusteringmethods
_version_	1718439310880931840

Exploring function prediction in protein interaction networks via clustering methods.

Ejemplares similares