Results 21 to 30 of about 3,867 (213)
namitaML/RelDenClu-Non-linear-feature-relation-based-biclustering: RelDenClu
, 2022 A novel biclustering ...
namitaML
core +1 more source
Unsupervised learning for medical data: A review of probabilistic factorization methods
Statistics in Medicine, Volume 42, Issue 30, Page 5541-5554, 30 December 2023., 2023 We review popular unsupervised learning methods for the analysis of high‐dimensional data encountered in, for example, genomics, medical imaging, cohort studies, and biobanks. We show that four commonly used methods, principal component analysis, K‐means clustering, nonnegative matrix factorization, and latent Dirichlet allocation, can be written as ...
Dorien Neijzen, Gerton Lunter
wiley +1 more source
, 2022 RelDenClu biclustering algorithm with application to real life ...
namitaML
core +1 more source
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
openaire +3 more sources
, 2022 RelDenClu is a non-linear feature dependence-based biclustering method.
namitaML
core +1 more source
A visual analytics approach for understanding biclustering results from microarray data
BMC Bioinformatics, 2008 Background Microarray analysis is an important area of bioinformatics. In the last few years, biclustering has become one of the most popular methods for classifying data from microarrays.
Quintales Luis +2 more
doaj +1 more source
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
Bayesian Biclustering on Discrete Data: Variable Selection Methods [PDF]
, 2013 Biclustering is a technique for clustering rows and columns of a data matrix simultaneously. Over the past few years, we have seen its applications in biology-related fields, as well as in many data mining projects.
Guo, Lei, Lei Guo
core +5 more sources
Implementation of BiClusO and its comparison with other biclustering algorithms
Applied Network Science, 2019 This paper describes the implementation of biclustering algorithm BiClusO using graphical user interface and different parameters to generate overlapping biclusters from a binary sparse matrix.
Mohammad Bozlul Karim +2 more
doaj +1 more source