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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Experimental correlation analysis of bicluster coherence measures and gene ontology information

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Autor(es):
Padilha, Victor Alexandre [1] ; de Leon Ferreira de Carvalho, Andre Carlos Ponce [1]
Número total de Autores: 2
Afiliação do(s) autor(es):
[1] Univ Sao Paulo, Inst Math & Comp Sci, Av Trabalhador Sao Carlense 400, BR-13566590 Sao Carlos, SP - Brazil
Número total de Afiliações: 1
Tipo de documento: Artigo Científico
Fonte: APPLIED SOFT COMPUTING; v. 85, DEC 2019.
Citações Web of Science: 0
Resumo

Biclustering algorithms have become popular tools for gene expression data analysis. They can identify local patterns defined by subsets of genes and subsets of samples, which cannot be detected by traditional clustering algorithms. In spite of being useful, biclustering is an NP-hard problem. Therefore, the majority of biclustering algorithms look for biclusters optimizing a pre-established coherence measure. Many heuristics and validation measures have been proposed for biclustering over the last 20 years. However, there is a lack of an extensive comparison of bicluster coherence measures on practical scenarios. To deal with this lack, this paper experimentally analyzes 17 bicluster coherence measures and external measures calculated from information obtained in the gene ontologies. In this analysis, results were produced by 10 algorithms from the literature in 19 gene expression datasets. According to the experimental results, a few pairs of strongly correlated coherence measures could be identified, which suggests redundancy. Moreover, the pairs of strongly correlated measures might change when dealing with normalized or non-normalized data and biclusters enriched by different ontologies. Finally, there was no clear relation between coherence measures and assessment using information from gene ontology. (AU)

Processo FAPESP: 16/18615-0 - Aprendizado de máquina avançado
Beneficiário:André Carlos Ponce de Leon Ferreira de Carvalho
Linha de fomento: Auxílio à Pesquisa - Parceria para Inovação Tecnológica - PITE
Processo FAPESP: 13/07375-0 - CeMEAI - Centro de Ciências Matemáticas Aplicadas à Indústria
Beneficiário:José Alberto Cuminato
Linha de fomento: Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs
Processo FAPESP: 17/02975-0 - Ensembles de resultados de bi-agrupamento
Beneficiário:Victor Alexandre Padilha
Linha de fomento: Bolsas no Brasil - Doutorado