Abstract—Co-clustering has not been much exploited in biomedical in-formatics, despite its success in other domains. Most of the previous ap-plications 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 paper. Index Terms—Acute myeloid leukemia, biomedical informatics, co-clustering, microRNA, single nucleotide polymorphism. I
In recent years, the use of gene expression data has expanded to many areas of medical research, dru...
Microarray analysis using clustering algorithms can suffer from lack of inter-method consistency i...
Motivation: Recent advancements in microarray technology allows simultaneous monitoring of the expre...
none3Co-clustering has not been much exploited in biomedical informatics, despite its success in oth...
For better understanding the genetic mechanisms underlying clinical observations, and better definin...
AbstractFor better understanding the genetic mechanisms underlying clinical observations, and better...
Cancer has been classified as a heterogeneous genetic disease comprising various different subtypes ...
Applications of clustering algorithms in biomedical research are ubiquitous, with typical examples i...
Recent advances in next-generation sequencing and computational technologies have enabled routine an...
Clustering is a long-standing problem in computer science and is applied in virtually any scientific...
Abstracts--Data Mining has become an important topic in effective analysis of gene expression data d...
Microarray analysis using clustering algorithms can suffer from lack of inter-method consistency in ...
Recent advances in next-generation sequencing and computational technologies have enabled routine an...
In data analysis, clustering is the process of finding groups in unlabelled data according to simila...
In gene expression profiling studies, including single-cell RNA sequencing (scRNA-seq) analyses, the...
In recent years, the use of gene expression data has expanded to many areas of medical research, dru...
Microarray analysis using clustering algorithms can suffer from lack of inter-method consistency i...
Motivation: Recent advancements in microarray technology allows simultaneous monitoring of the expre...
none3Co-clustering has not been much exploited in biomedical informatics, despite its success in oth...
For better understanding the genetic mechanisms underlying clinical observations, and better definin...
AbstractFor better understanding the genetic mechanisms underlying clinical observations, and better...
Cancer has been classified as a heterogeneous genetic disease comprising various different subtypes ...
Applications of clustering algorithms in biomedical research are ubiquitous, with typical examples i...
Recent advances in next-generation sequencing and computational technologies have enabled routine an...
Clustering is a long-standing problem in computer science and is applied in virtually any scientific...
Abstracts--Data Mining has become an important topic in effective analysis of gene expression data d...
Microarray analysis using clustering algorithms can suffer from lack of inter-method consistency in ...
Recent advances in next-generation sequencing and computational technologies have enabled routine an...
In data analysis, clustering is the process of finding groups in unlabelled data according to simila...
In gene expression profiling studies, including single-cell RNA sequencing (scRNA-seq) analyses, the...
In recent years, the use of gene expression data has expanded to many areas of medical research, dru...
Microarray analysis using clustering algorithms can suffer from lack of inter-method consistency i...
Motivation: Recent advancements in microarray technology allows simultaneous monitoring of the expre...