ReviewPloS one2015
Quality measures for gene expression biclusters.
Review in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Semantic locality-aware biclustering for brain functional network connectivity.Scientific reports · 2025Article
- Online-adjusted evolutionary biclustering algorithm to identify significant modules in gene expression data.Briefings in bioinformatics · 2024Article
- SpaDE: Semantic Locality Preserving Biclustering for Neuroimaging Data.bioRxiv : the preprint server for biology · 2024Article
- Biclustering data analysis: a comprehensive survey.Briefings in bioinformatics · 2024Review
- Biclustering of Log Data: Insights from a Computer-Based Complex Problem Solving Assessment.Journal of Intelligence · 2024Article
- Biclustering fMRI time series: a comparative study.BMC bioinformatics · 2022Article
- N-BiC: A Method for Multi-Component and Symptom Biclustering of Structural MRI Data: Application to Schizophrenia.IEEE transactions on bio-medical engineering · 2020Article
- TuBA: Tunable biclustering algorithm reveals clinically relevant tumor transcriptional profiles in breast cancer.GigaScience · 2019Article
- The Translational Potential of Neuroimaging Genomic Analyses To Diagnosis And Treatment In The Mental Disorders.Proceedings of the IEEE. Institute of Electrical and Electronics Engineers · 2019Article
- EBIC: an evolutionary-based parallel biclustering algorithm for pattern discovery.Bioinformatics (Oxford, England) · 2018Article
- Identifying Multi-Dimensional Co-Clusters in Tensors Based on Hyperplane Detection in Singular Vector Spaces.PloS one · 2016Article
- BiCAMWI: A Genetic-Based Biclustering Algorithm for Detecting Dynamic Protein Complexes.PloS one · 2016Article
- Linking Genes to Cardiovascular Diseases: Gene Action and Gene-Environment Interactions.Journal of cardiovascular translational research · 2015Review
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Authors and funding
3 authors.
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Abstract
An noticeable number of biclustering approaches have been proposed proposed for the study of gene expression data, especially for discovering functionally related gene sets under different subsets of experimental conditions. In this context, recognizing groups of co-expressed or co-regulated genes, that is, genes which follow a similar expression pattern, is one of the main objectives. Due to the problem complexity, heuristic searches are usually used instead of exhaustive algorithms. Furthermore, most of biclustering approaches use a measure or cost function that determines the quality of biclusters. Having a suitable quality metric for bicluster is a critical aspect, not only for guiding the search, but also for establishing a comparison criteria among the results obtained by different biclustering techniques. In this paper, we analyse a large number of existing approaches to quality measures for gene expression biclusters, as well as we present a comparative study of them based on their capability to recognize different expression patterns in biclusters.
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