2010
DOI: 10.1093/bioinformatics/btq227
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FABIA: factor analysis for bicluster acquisition

Abstract: Motivation: Biclustering of transcriptomic data groups genes and samples simultaneously. It is emerging as a standard tool for extracting knowledge from gene expression measurements. We propose a novel generative approach for biclustering called ‘FABIA: Factor Analysis for Bicluster Acquisition’. FABIA is based on a multiplicative model, which accounts for linear dependencies between gene expression and conditions, and also captures heavy-tailed distributions as observed in real-world transcriptomic data. The … Show more

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Cited by 271 publications
(287 citation statements)
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References 36 publications
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“…This paper introduces a novel algorithm, where the rationale is to decompose the data matrix into levels, each corresponding to a different bicluster (as in [13,4]), thus allowing to obtain non-exhaustive and possibly overlapping biclusters. In this class of approaches, sparsity plays a crucial role.…”
Section: Overview Of the Proposed Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…This paper introduces a novel algorithm, where the rationale is to decompose the data matrix into levels, each corresponding to a different bicluster (as in [13,4]), thus allowing to obtain non-exhaustive and possibly overlapping biclusters. In this class of approaches, sparsity plays a crucial role.…”
Section: Overview Of the Proposed Methodsmentioning
confidence: 99%
“…A recent trend is to use matrix factorization tools [4,11,13,19,31,44], most of which relying on the concept of latent block models [11,26,31,34,44]. These approaches simultaneously arrange the rows and columns of a data matrix into groups of similar response patterns, thus yielding biclusters where the rows/columns belong to only one group, and the data matrix is divided into exhaustive and non overlapping biclusters.…”
Section: Biclusteringmentioning
confidence: 99%
“…The generative framework allows to utilize well-founded model selection methods and to apply Bayesian techniques (Hochreiter et al, 2010). In this present study, we performed bicluster analysis using fabia to classify the genes in the progression and identified the differentially expressed genes among healthy liver samples, cirrhotic samples and HCC samples.…”
Section: Introductionmentioning
confidence: 99%
“…Além disso, em um conjunto de dados transcriptômicos múltiplas vias genéticas (genetic pathways) podem estar ativos em uma condição e um gene pode participar de diferentes caminhos genéticos sob condições distintas. Para detectar essas interações entre genes, os bi-grupos precisam se sobrepor (Hochreiter et al, 2010). O mesmo pode ser dito sobre documentos textuais descritos por conjuntos de palavras (bag of words), nos quais um documento pode pertencer a categorias diferentes dependendo das palavras consideradas.…”
Section: Bi-agrupamentounclassified
“…No entanto, os ín-dices clássicos de agrupamento de dados não são diretamente aplicáveis em bi-agrupamentos, i.e., em soluções produzidas por algoritmos de bi-agrupamento. Alguns índices de validação externa de bi-agrupamento foram recentemente propostos (Liu e Wang, 2007;Santamaría et al, 2007;Lee et al, 2009;Hochreiter et al, 2010), invariavelmente em trabalhos nos quais a principal contribuição foi a proposição de um novo algoritmo de bi-agrupamento ou a comparação experimental de algoritmos de bi-agrupamento. Como consequência, propriedades importantes que um índice de validação externa deve possuir foram negligenciadas, acarretando em problemas críticos identificados durante este trabalho.…”
Section: Introductionunclassified