ArticleAlgorithms for molecular biology : AMB2011
DeBi: Discovering Differentially Expressed Biclusters using a Frequent Itemset Approach.
Article in Algorithms for molecular biology : AMB, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed, 67 citations in OpenAlex.
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- Gracob: a novel graph-based constant-column biclustering method for mining growth phenotype data.Bioinformatics (Oxford, England) · 2017Article
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- A systematic comparative evaluation of biclustering techniques.BMC bioinformatics · 2017Article
- BicNET: Flexible module discovery in large-scale biological networks using biclustering.Algorithms for molecular biology : AMB · 2016Article
- BiC2PAM: constraint-guided biclustering for biological data analysis with domain knowledge.Algorithms for molecular biology : AMB · 2016Article
- A hierarchical Bayesian model for flexible module discovery in three-way time-series data.Bioinformatics (Oxford, England) · 2015Article
- A primer to frequent itemset mining for bioinformatics.Briefings in bioinformatics · 2015Review
- Efficient Mining of Discriminative Co-clusters from Gene Expression Data.Knowledge and information systems · 2014Article
- BicSPAM: flexible biclustering using sequential patterns.BMC bioinformatics · 2014Article
- biDCG: a new method for discovering global features of DNA microarray data via an iterative re-clustering procedure.PloS one · 2014Article
- BicPAM: Pattern-based biclustering for biomedical data analysis.Algorithms for molecular biology : AMB · 2014Article
- Configurable pattern-based evolutionary biclustering of gene expression data.Algorithms for molecular biology : AMB · 2013Article
- SiBIC: a web server for generating gene set networks based on biclusters obtained by maximal frequent itemset mining.PloS one · 2013Article
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Authors and funding
2 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundThe analysis of massive high throughput data via clustering algorithms is very important for elucidating gene functions in biological systems. However, traditional clustering methods have several drawbacks. Biclustering overcomes these limitations by grouping genes and samples simultaneously. It discovers subsets of genes that are co-expressed in certain samples. Recent studies showed that biclustering has a great potential in detecting marker genes that are associated with certain tissues or diseases. Several biclustering algorithms have been proposed. However, it is still a challenge to find biclusters that are significant based on biological validation measures. Besides that, there is a need for a biclustering algorithm that is capable of analyzing very large datasets in reasonable time.
resultsHere we present a fast biclustering algorithm called DeBi (Differentially Expressed BIclusters). The algorithm is based on a well known data mining approach called frequent itemset. It discovers maximum size homogeneous biclusters in which each gene is strongly associated with a subset of samples. We evaluate the performance of DeBi on a yeast dataset, on synthetic datasets and on human datasets.
conclusionsWe demonstrate that the DeBi algorithm provides functionally more coherent gene sets compared to standard clustering or biclustering algorithms using biological validation measures such as Gene Ontology term and Transcription Factor Binding Site enrichment. We show that DeBi is a computationally efficient and powerful tool in analyzing large datasets. The method is also applicable on multiple gene expression datasets coming from different labs or platforms.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.