ReviewBriefings in bioinformatics2019
It is time to apply biclustering: a comprehensive review of biclustering applications in biological and biomedical data.
Review in Briefings in bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
23 citing papers in PubMed, 80 citations in OpenAlex.
- A formal explanation space for the simultaneous clustering of neurologic diseases based on their signs and symptoms.BMC medical informatics and decision making · 2025Article
- A personalized reinforcement learning recommendation algorithm using bi-clustering techniques.PloS one · 2025Article
- Outcome-guided spike-and-slab Lasso Biclustering: A Novel Approach for Enhancing Biclustering Techniques for Gene Expression Analysis.Statistics and computing · 2025Article
- Heterogeneity analysis provides evidence for a genetically homogeneous subtype of bipolar-disorder.PloS one · 2025Article
- Heterogeneity analysis provides evidence for a genetically homogeneous subtype of bipolar-disorder.ArXiv · 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
- Dual ICA to extract interacting sets of genes and conditions from transcriptomic data.ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine · 2023Article
- Pipeline for characterizing alternative mechanisms (PCAM) based on bi-clustering to study colorectal cancer heterogeneity.Computational and structural biotechnology journal · 2023Article
- Topology and redescriptions detect multiple alternative biological pathways from clinical phenotypes.Experimental biology and medicine (Maywood, N.J.) · 2022Article
- Biclustering fMRI time series: a comparative study.BMC bioinformatics · 2022Article
- Biclustering analysis of functionals via penalized fusion.Journal of multivariate analysis · 2022Article
- DISA tool: Discriminative and informative subspace assessment with categorical and numerical outcomes.PloS one · 2022Article
- A Multi-Level Iterative Bi-Clustering Method for Discovering miRNA Co-regulation Network of Abiotic Stress Tolerance in Soybeans.Frontiers in plant science · 2022Article
- miRSM: an R package to infer and analyse miRNA sponge modules in heterogeneous data.RNA biology · 2021Article
- Article
- Discovering a sparse set of pairwise discriminating features in high-dimensional data.Bioinformatics (Oxford, England) · 2021Article
- ORN: Inferring patient-specific dysregulation status of pathway modules in cancer with OR-gate Network.PLoS computational biology · 2021Article
- Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment.Frontiers in microbiology · 2021Review
- BEM: Mining Coregulation Patterns in Transcriptomics via Boolean Matrix Factorization.Bioinformatics (Oxford, England) · 2020Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
Biclustering is a powerful data mining technique that allows clustering of rows and columns, simultaneously, in a matrix-format data set. It was first applied to gene expression data in 2000, aiming to identify co-expressed genes under a subset of all the conditions/samples. During the past 17 years, tens of biclustering algorithms and tools have been developed to enhance the ability to make sense out of large data sets generated in the wake of high-throughput omics technologies. These algorithms and tools have been applied to a wide variety of data types, including but not limited to, genomes, transcriptomes, exomes, epigenomes, phenomes and pharmacogenomes. However, there is still a considerable gap between biclustering methodology development and comprehensive data interpretation, mainly because of the lack of knowledge for the selection of appropriate biclustering tools and further supporting computational techniques in specific studies. Here, we first deliver a brief introduction to the existing biclustering algorithms and tools in public domain, and then systematically summarize the basic applications of biclustering for biological data and more advanced applications of biclustering for biomedical data. This review will assist researchers to effectively analyze their big data and generate valuable biological knowledge and novel insights with higher efficiency.
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What OpenQuestion holds
Registered trials
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.