We tested whether Self-Organizing Maps (SOMs) could be used to effectively integrate, visualize, and mine diverse genomics data types, including complex chromatin signatures. A fine-grained SOM was trained on 72 ChIP-seq histone modifications and DNase-seq datasets from six biologically diverse cell lines studied by the ENCODE Project Consortium. We mined the resulting SOM to identify chromatin signatures related to sequence-specific transcription factor occupancy, sequence motif enrichment, and biological functions. To highlight clusters enriched for specific functions such as transcriptional promoters or enhancers, we overlaid onto the map additional datasets no
* to whom correspondence should be addressed Background: Self organizing maps (SOM) enable the strai...
The genetic architecture of complex traits is multifactorial. Genome-wide association studies (GWASs...
With remarkable increase of genomic sequence data of a wide range of species, novel tools are needed...
We tested whether self-organizing maps (SOMs) could be used to effectively integrate, visualize, and...
specificity using self-organizing maps Integrating and mining the chromatin landscape of cell-typ
cDNA microarrays permit massively parallel gene expression analysis and have spawned a new paradigm ...
Motivation: Self-Organizing Maps (SOMs) are readily-available bioinformatics methods for clustering ...
Modern high-throughput technologies such as microarrays, next generation sequencing and mass spectro...
Self organizing maps (SOMs) portrait molecular phenotypes with individual resolution. We demonstrate...
Self organizing maps (SOMs) portrait molecular phenotypes with individual resolution. We demonstrate...
AbstractDNA microarray technologies together with rapidly increasing genomic sequence information is...
A self-organizing map (SOM) is an artificial neural network algorithm that can learn from the traini...
A method to evaluate and analyze the massive data generated by series of microarray experiments is o...
This paper introduces a comprehensive review of a Growing Hierarchical Self-Organizing Map (GHSOM) r...
We report on the development of an unsupervised algorithm for the genome-wide discovery and ana-lysi...
* to whom correspondence should be addressed Background: Self organizing maps (SOM) enable the strai...
The genetic architecture of complex traits is multifactorial. Genome-wide association studies (GWASs...
With remarkable increase of genomic sequence data of a wide range of species, novel tools are needed...
We tested whether self-organizing maps (SOMs) could be used to effectively integrate, visualize, and...
specificity using self-organizing maps Integrating and mining the chromatin landscape of cell-typ
cDNA microarrays permit massively parallel gene expression analysis and have spawned a new paradigm ...
Motivation: Self-Organizing Maps (SOMs) are readily-available bioinformatics methods for clustering ...
Modern high-throughput technologies such as microarrays, next generation sequencing and mass spectro...
Self organizing maps (SOMs) portrait molecular phenotypes with individual resolution. We demonstrate...
Self organizing maps (SOMs) portrait molecular phenotypes with individual resolution. We demonstrate...
AbstractDNA microarray technologies together with rapidly increasing genomic sequence information is...
A self-organizing map (SOM) is an artificial neural network algorithm that can learn from the traini...
A method to evaluate and analyze the massive data generated by series of microarray experiments is o...
This paper introduces a comprehensive review of a Growing Hierarchical Self-Organizing Map (GHSOM) r...
We report on the development of an unsupervised algorithm for the genome-wide discovery and ana-lysi...
* to whom correspondence should be addressed Background: Self organizing maps (SOM) enable the strai...
The genetic architecture of complex traits is multifactorial. Genome-wide association studies (GWASs...
With remarkable increase of genomic sequence data of a wide range of species, novel tools are needed...