[HTML][HTML] Biclustering on expression data: A review
Biclustering has become a popular technique for the study of gene expression data,
especially for discovering functionally related gene sets under different subsets of …
especially for discovering functionally related gene sets under different subsets of …
Metaheuristic Biclustering Algorithms: From State-of-the-Art to Future Opportunities
A José-García, J Jacques, V Sobanski… - ACM Computing …, 2023 - dl.acm.org
Biclustering is an unsupervised machine-learning technique that simultaneously clusters
rows and columns in a data matrix. Over the past two decades, the field of biclustering has …
rows and columns in a data matrix. Over the past two decades, the field of biclustering has …
Stellar-mass black hole optimization for biclustering microarray gene expression data
R Balamurugan, AM Natarajan… - Applied Artificial …, 2015 - Taylor & Francis
DNA microarray gene expression data analysis has provided new insights into gene
function, disease pathophysiology, disease classification, and drug development …
function, disease pathophysiology, disease classification, and drug development …
Biclustering algorithms based on metaheuristics: a review
A José-García, J Jacques, V Sobanski… - … for machine learning …, 2022 - Springer
Biclustering is an unsupervised machine learning technique that simultaneously clusters
rows and columns in a data matrix. Biclustering has emerged as an important approach and …
rows and columns in a data matrix. Biclustering has emerged as an important approach and …
Co-clustering neighborhood—based collaborative filtering framework using formal concept analysis
S Kataria, U Batra - International Journal of Information Technology, 2022 - Springer
In recent times information on the web is growing exponentially and reaching to the point
where humans can no longer deal with it manually or with conventional techniques …
where humans can no longer deal with it manually or with conventional techniques …
Predicting missing values with biclustering: A coherence-based approach
In this work, a novel biclustering-based approach to data imputation is proposed. This
approach is based on the Mean Squared Residue metric, used to evaluate the degree of …
approach is based on the Mean Squared Residue metric, used to evaluate the degree of …
A collaborative filtering recommendation algorithm based on biclustering
J Wang, H Song, X Zhou - 2015 IEEE International Conference …, 2015 - ieeexplore.ieee.org
Collaborative filtering has been widely used in many fields such as movie recommendation
and e-commerce. However, there are still some problems such as data sparsity which …
and e-commerce. However, there are still some problems such as data sparsity which …
Configurable pattern-based evolutionary biclustering of gene expression data
Background Biclustering algorithms for microarray data aim at discovering functionally
related gene sets under different subsets of experimental conditions. Due to the problem …
related gene sets under different subsets of experimental conditions. Due to the problem …
A hash-based co-clustering algorithm for categorical data
FO de França - Expert Systems with Applications, 2016 - Elsevier
Cluster analysis, or clustering, refers to the analysis of the structural organization of a data
set. This analysis is performed by grouping together objects of the data that are more similar …
set. This analysis is performed by grouping together objects of the data that are more similar …
Conceptual and practical aspects of the aiNet family of algorithms
In this paper, a review of the conceptual and practical aspects of the aiNet (Artificial Immune
Network) family of algorithms will be provided. This family of algorithms started with the aiNet …
Network) family of algorithms will be provided. This family of algorithms started with the aiNet …