Results 31 to 40 of about 3,388 (214)
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
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
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
doaj +1 more source
, 2022 Feature dependence-based biclustering with application to real-life ...
namitaML
core +1 more source
Austrian Journal of Statistics, 2020 Biclustering, in general, is a process of clustering genes and conditions simultaneously rather than clustering them separately. The purpose of biclustering is to discover a subset from experimental data.
Alhadi Bustamam +3 more
doaj +1 more source
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
Possibilistic approach to biclustering: an application to oligonucleotide microarray data analysis [PDF]
, 2006 The important research objective of identifying genes with similar behavior with respect to different conditions has recently been tackled with biclustering techniques.
Mitra, S. +14 more
core +1 more source
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
openaire +2 more sources
Biometrics, Volume 79, Issue 4, Page 3846-3858, December 2023., 2023 Abstract Clustering has long been a popular unsupervised learning approach to identify groups of similar objects and discover patterns from unlabeled data in many applications. Yet, coming up with meaningful interpretations of the estimated clusters has often been challenging precisely due to their unsupervised nature.
Minjie Wang +2 more
wiley +1 more source