ArticleBMC bioinformatics2012
CLAG: an unsupervised non hierarchical clustering algorithm handling biological data.
Article in BMC bioinformatics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
- The S100A7 nuclear interactors in autoimmune diseases: a coevolutionary study in mammals.Immunogenetics · 2022Article
- COVTree: Coevolution in OVerlapped sequences by Tree analysis server.Nucleic acids research · 2020Article
- S100A7/Ran-binding protein 9 coevolution in mammals.Immunogenetics · 2020Article
- Large-Scale Comparison of Toxin and Antitoxins inToxins · 2020Article
- Coevolution analysis of amino-acids reveals diversified drug-resistance solutions in viral sequences: a case study of hepatitis B virus.Virus evolution · 2020Article
- CoevDB: a database of intramolecular coevolution among protein-coding genes of the bony vertebrates.Nucleic acids research · 2019Article
- BIS2Analyzer: a server for co-evolution analysis of conserved protein families.Nucleic acids research · 2017Article
- Coevolution analysis of Hepatitis C virus genome to identify the structural and functional dependency network of viral proteins.Scientific reports · 2016Article
- A method for clustering of miRNA sequences using fragmented programming.Bioinformation · 2016Article
- miR-3646 promotes cell proliferation, migration, and invasion via regulating G2/M transition in human breast cancer cells.American journal of translational research · 2016Article
- MiR-300 regulate the malignancy of breast cancer by targeting p53.International journal of clinical and experimental medicine · 2015Article
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2 authors.
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Abstract
backgroundSearching for similarities in a set of biological data is intrinsically difficult due to possible data points that should not be clustered, or that should group within several clusters. Under these hypotheses, hierarchical agglomerative clustering is not appropriate. Moreover, if the dataset is not known enough, like often is the case, supervised classification is not appropriate either.
resultsCLAG (for CLusters AGgregation) is an unsupervised non hierarchical clustering algorithm designed to cluster a large variety of biological data and to provide a clustered matrix and numerical values indicating cluster strength. CLAG clusterizes correlation matrices for residues in protein families, gene-expression and miRNA data related to various cancer types, sets of species described by multidimensional vectors of characters, binary matrices. It does not ask to all data points to cluster and it converges yielding the same result at each run. Its simplicity and speed allows it to run on reasonably large datasets.
conclusionsCLAG can be used to investigate the cluster structure present in biological datasets and to identify its underlying graph. It showed to be more informative and accurate than several known clustering methods, as hierarchical agglomerative clustering, k-means, fuzzy c-means, model-based clustering, affinity propagation clustering, and not to suffer of the convergence problem proper to this latter.
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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.