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Fast Biclustering by Dual Parameterization [PDF]
, 2015 We study two clustering problems, Starforest Editing, the problem of adding and deleting edges to obtain a disjoint union of stars, and the generalization Bicluster Editing.
Drange, Pål Grønås +3 more
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Differential co-expression framework to quantify goodness of biclusters and compare biclustering algorithms [PDF]
Algorithms for Molecular Biology, 2010 Background Biclustering is an important analysis procedure to understand the biological mechanisms from microarray gene expression data. Several algorithms have been proposed to identify biclusters, but very little effort was made to compare the ...
Karuturi R Krishna Murthy, Chia Burton
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RUBic: rapid unsupervised biclustering. [PDF]
BMC Bioinformatics, 2023 AbstractBiclustering of biologically meaningful binary information is essential in many applications related to drug discovery, like protein–protein interactions and gene expressions. However, for robust performance in recently emerging large health datasets, it is important for new biclustering algorithms to be scalable and fast.
Sriwastava BK +3 more
europepmc +4 more sources
Biometrics, 2016 SummaryIn the biclustering problem, we seek to simultaneously group observations and features. While biclustering has applications in a wide array of domains, ranging from text mining to collaborative filtering, the problem of identifying structure in high-dimensional genomic data motivates this work.
Chi, Eric C. +2 more
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Biclustering via Semiparametric Bayesian Inference [PDF]
Bayesian Analysis, 2022 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Murua, Alejandro +1 more
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A Bayesian hierarchical hidden Markov model for clustering and gene selection: Application to kidney cancer gene expression data. [PDF]
Biom JAbstract We introduce a Bayesian approach for biclustering that accounts for the prior functional dependence between genes using hidden Markov models (HMMs). We utilize biological knowledge gathered from gene ontologies and the hidden Markov structure to capture the potential coexpression of neighboring genes.
Chekouo T, Mukherjee H.
europepmc +2 more sources
Biclustering with heterogeneous variance [PDF]
Proceedings of the National Academy of Sciences, 2013 In cancer research, as in all of medicine, it is important to classify patients into etiologically and therapeutically relevant subtypes to improve diagnosis and treatment. One way to do this is to use clustering methods to find subgroups of homogeneous individuals based on genetic profiles together with heuristic clinical analysis.
Chen, Guanhua +2 more
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Identification of bicluster regions in a binary matrix and its applications. [PDF]
PLoS ONE, 2013 Biclustering has emerged as an important approach to the analysis of large-scale datasets. A biclustering technique identifies a subset of rows that exhibit similar patterns on a subset of columns in a data matrix.
Hung-Chia Chen +3 more
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Profile likelihood biclustering
Electronic Journal of Statistics, 2020 Biclustering, the process of simultaneously clustering the rows and columns of a data matrix, is a popular and effective tool for finding structure in a high-dimensional dataset. Many biclustering procedures appear to work well in practice, but most do not have associated consistency guarantees.
Flynn, Cheryl, Perry, Patrick
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Journal of King Saud University: Computer and Information Sciences, 2022 Background: Ensemble biclustering comprises a class of biclustering algorithms that generates a consensus, better-quality partition/s as output. This concept has emerged from the fusion of existing biclustering methods hybridized upon selected aspects ...
Bhawani Sankar Biswal +2 more
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