none3Co-clustering has not been much exploited in biomedical informatics, despite its success in other domains, and most previous applications were limited to analyzing gene expression data. We performed co-clustering analysis on other types of data and obtained promising results, as summarized in this article.noneS. Yoon; L. Benini; G. De MicheliS. Yoon; L. Benini; G. De Michel
Abstract. The huge volume of gene expression data produced by mi-croarrays and other high-throughput...
A new unsupervised gene clustering algorithm based on the integration of biological knowledge into e...
Methods for high-dimensional data clustering represents a prolific research area in data mining, enc...
Abstract—Co-clustering has not been much exploited in biomedical in-formatics, despite its success i...
For better understanding the genetic mechanisms underlying clinical observations, and better definin...
AbstractFor better understanding the genetic mechanisms underlying clinical observations, and better...
Clustering is a long-standing problem in computer science and is applied in virtually any scientific...
We present a novel co-clustering method using co-variates with application to genomic data
Cancer has been classified as a heterogeneous genetic disease comprising various different subtypes ...
none2noThe analysis of microarray data is a widespread functional genomics approach that allows for ...
Applications of clustering algorithms in biomedical research are ubiquitous, with typical examples i...
Microarray analysis using clustering algorithms can suffer from lack of inter-method consistency in ...
Identifying co-expressed gene clusters can provide evidence for genetic or physical interactions. Th...
Abstracts--Data Mining has become an important topic in effective analysis of gene expression data d...
Abstract. Motivation: Many clustering algorithms have been proposed for the analysis of gene expr...
Abstract. The huge volume of gene expression data produced by mi-croarrays and other high-throughput...
A new unsupervised gene clustering algorithm based on the integration of biological knowledge into e...
Methods for high-dimensional data clustering represents a prolific research area in data mining, enc...
Abstract—Co-clustering has not been much exploited in biomedical in-formatics, despite its success i...
For better understanding the genetic mechanisms underlying clinical observations, and better definin...
AbstractFor better understanding the genetic mechanisms underlying clinical observations, and better...
Clustering is a long-standing problem in computer science and is applied in virtually any scientific...
We present a novel co-clustering method using co-variates with application to genomic data
Cancer has been classified as a heterogeneous genetic disease comprising various different subtypes ...
none2noThe analysis of microarray data is a widespread functional genomics approach that allows for ...
Applications of clustering algorithms in biomedical research are ubiquitous, with typical examples i...
Microarray analysis using clustering algorithms can suffer from lack of inter-method consistency in ...
Identifying co-expressed gene clusters can provide evidence for genetic or physical interactions. Th...
Abstracts--Data Mining has become an important topic in effective analysis of gene expression data d...
Abstract. Motivation: Many clustering algorithms have been proposed for the analysis of gene expr...
Abstract. The huge volume of gene expression data produced by mi-croarrays and other high-throughput...
A new unsupervised gene clustering algorithm based on the integration of biological knowledge into e...
Methods for high-dimensional data clustering represents a prolific research area in data mining, enc...