Integrating biological knowledge based on functional annotations for biclustering of gene expression data

Nepomuceno, Juan A.; Troncoso, Alicia; Nepomuceno-Chamorro, Isabel A.; Aguilar–Ruiz, Jesús S.

doi:10.1016/j.cmpb.2015.02.010

Cited by 31 publications

(22 citation statements)

References 45 publications

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“…Scatter search carries out a number of fitness function evaluations during the search less than other evolutionary metaheuristics [ 8 ]. This paper follows the preliminary ideas presented in [ 9 ] and it extends the work previously published in [ 10 ], where a particular gene parwise GO measure was studied in the context of biclustering. The impact of biological information integration in the context of high-dimensional datasets is analyzed for the first time in this paper to the best of our knowledge.…”

Section: Introductionmentioning

confidence: 67%

Pairwise gene GO-based measures for biclustering of high-dimensional expression data

et al. 2018

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BackgroundBiclustering algorithms search for groups of genes that share the same behavior under a subset of samples in gene expression data. Nowadays, the biological knowledge available in public repositories can be used to drive these algorithms to find biclusters composed of groups of genes functionally coherent. On the other hand, a distance among genes can be defined according to their information stored in Gene Ontology (GO). Gene pairwise GO semantic similarity measures report a value for each pair of genes which establishes their functional similarity. A scatter search-based algorithm that optimizes a merit function that integrates GO information is studied in this paper. This merit function uses a term that addresses the information through a GO measure.ResultsThe effect of two possible different gene pairwise GO measures on the performance of the algorithm is analyzed. Firstly, three well known yeast datasets with approximately one thousand of genes are studied. Secondly, a group of human datasets related to clinical data of cancer is also explored by the algorithm. Most of these data are high-dimensional datasets composed of a huge number of genes. The resultant biclusters reveal groups of genes linked by a same functionality when the search procedure is driven by one of the proposed GO measures. Furthermore, a qualitative biological study of a group of biclusters show their relevance from a cancer disease perspective.ConclusionsIt can be concluded that the integration of biological information improves the performance of the biclustering process. The two different GO measures studied show an improvement in the results obtained for the yeast dataset. However, if datasets are composed of a huge number of genes, only one of them really improves the algorithm performance. This second case constitutes a clear option to explore interesting datasets from a clinical point of view.

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Section: Introductionmentioning

confidence: 67%

Pairwise gene GO-based measures for biclustering of high-dimensional expression data

et al. 2018

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“…Moreover, the biclusters found by this algorithm not only covers all the conditions, but also some biclusters covers fewer conditions, that is, the bicluster individuals with higher levels of coexpression can be found. [31] can investigate the biological relevance of the acquired biclusters. GO function analysis is used to enrich the results of biclusters.…”

Section: 2 Comparison Of Results Between This Algorithm and Grasp-mentioning

confidence: 99%

Optimization of Biclustering Algorithm Based on Greedy Randomized Adaptive Search Procedure

Yang¹,

Zhao²,

Shu³

et al. 2018

JMI

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The biclustering algorithm based on greedy randomized adaptive search procedure (GRASP) has two main steps, firstly, bicluster seeds are generated by Kmeans clustering; secondly, the greedy randomized adaptive search procedure is used to expand these bicluster seeds so that to get some better biclusters. However, K-means clustering assumes that each gene belongs to only one cluster, which is not biologically valid, because some genes may not belong to only one category, and K-means clustering requires multiple runs to determine the number of "clusters," all of which will affect the identification of bicluster seeds. This paper optimizes GRASP-based bIclustering algorithm. In this paper, the single column vector clustering is used to generate biclusters seed, and then the bicluster seeds are added more rows and columns to generate the final biclusters. Experiment shows that this algorithm can generate a large number of biclusters with coexpression level, and the algorithm also finds more biclusters.

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“…The work by Nepomuceno et al . ( 60 ) meanwhile inputs biological annotation data into the fitness function to identify biclusters. Most biclustering methods would use this annotation data to validate found biclusters ( 37 ), Nepomuceno et al .…”

Section: Discussionmentioning

confidence: 99%

“…Most biclustering methods would use this annotation data to validate found biclusters ( 37 ), Nepomuceno et al . ( 60 ) however argues that their approach is less likely to identify biclusters composed of co-expressed genes that are the result of independent activation.…”

Section: Discussionmentioning

confidence: 99%

MCbiclust: a novel algorithm to discover large-scale functionally related gene sets from massive transcriptomics data collections

Bentham

Bryson

Szabadkai

2017

Nucleic Acids Research

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The potential to understand fundamental biological processes from gene expression data has grown in parallel with the recent explosion of the size of data collections. However, to exploit this potential, novel analytical methods are required, capable of discovering large co-regulated gene networks. We found current methods limited in the size of correlated gene sets they could discover within biologically heterogeneous data collections, hampering the identification of multi-gene controlled fundamental cellular processes such as energy metabolism, organelle biogenesis and stress responses. Here we describe a novel biclustering algorithm called Massively Correlated Biclustering (MCbiclust) that selects samples and genes from large datasets with maximal correlated gene expression, allowing regulation of complex networks to be examined. The method has been evaluated using synthetic data and applied to large bacterial and cancer cell datasets. We show that the large biclusters discovered, so far elusive to identification by existing techniques, are biologically relevant and thus MCbiclust has great potential in the analysis of transcriptomics data to identify large-scale unknown effects hidden within the data. The identified massive biclusters can be used to develop improved transcriptomics based diagnosis tools for diseases caused by altered gene expression, or used for further network analysis to understand genotype-phenotype correlations.

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Integrating biological knowledge based on functional annotations for biclustering of gene expression data

Cited by 31 publications

References 45 publications

Pairwise gene GO-based measures for biclustering of high-dimensional expression data

Pairwise gene GO-based measures for biclustering of high-dimensional expression data

Optimization of Biclustering Algorithm Based on Greedy Randomized Adaptive Search Procedure

MCbiclust: a novel algorithm to discover large-scale functionally related gene sets from massive transcriptomics data collections

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