A novel clustering approach for biological data using a new distance based on Gene Ontology

When applying clustering algorithms on biological data the information about biological processes is not usually present in an explicit way, although this knowledge is later used by biologists to validate the clusters and the relations found among data. This work presents a new distance measure for...

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Detalles Bibliográficos
Autores principales: Leale, Guillermo, Milone, Diego H., Bayá, Ariel E., Granitto, Pablo Miguel, Stegmayer, Georgina
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2013
Materias:
Ciencias Informáticas
biological data
clustering algorithm
measures
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/76213
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-76213
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
biological data
clustering algorithm
measures
spellingShingle Ciencias Informáticas
biological data
clustering algorithm
measures
Leale, Guillermo
Milone, Diego H.
Bayá, Ariel E.
Granitto, Pablo Miguel
Stegmayer, Georgina
A novel clustering approach for biological data using a new distance based on Gene Ontology
topic_facet Ciencias Informáticas
biological data
clustering algorithm
measures
description When applying clustering algorithms on biological data the information about biological processes is not usually present in an explicit way, although this knowledge is later used by biologists to validate the clusters and the relations found among data. This work presents a new distance measure for biological data which combines expression and semantic information, in order to be used into a clustering algorithm. The distance is calculated pairwise among all pairs of genes and it is incorporated during the training process of the clustering algorithm. The approach was evaluated on two real datasets using several validation measures. The obtained results are consistent across all the measures, showing better semantic quality for clusters with the new algorithm in comparison to standard clustering.
format Objeto de conferencia
Objeto de conferencia
author Leale, Guillermo
Milone, Diego H.
Bayá, Ariel E.
Granitto, Pablo Miguel
Stegmayer, Georgina
author_facet Leale, Guillermo
Milone, Diego H.
Bayá, Ariel E.
Granitto, Pablo Miguel
Stegmayer, Georgina
author_sort Leale, Guillermo
title A novel clustering approach for biological data using a new distance based on Gene Ontology
title_short A novel clustering approach for biological data using a new distance based on Gene Ontology
title_full A novel clustering approach for biological data using a new distance based on Gene Ontology
title_fullStr A novel clustering approach for biological data using a new distance based on Gene Ontology
title_full_unstemmed A novel clustering approach for biological data using a new distance based on Gene Ontology
title_sort novel clustering approach for biological data using a new distance based on gene ontology
publishDate 2013
url http://sedici.unlp.edu.ar/handle/10915/76213
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