Self-organizing maps with variable neighborhoods facilitate learning of chromatin accessibility signal shapes associated with regulatory elements

Background: Assigning chromatin states genome-wide (e.g. promoters, enhancers, etc.) is commonly performed to improve functional interpretation of these states. However, computational methods to assign chromatin state suffer from the following drawbacks: they typically require data from multiple ass...

Full description

Bibliographic Details
Main Authors:	Chan, J. (Author), Eicher, T. (Author), Luu, H. (Author), Machiraju, R. (Author), Mathé, E.A (Author)
Format:	Article
Language:	English
Published:	BioMed Central Ltd 2021
Subjects:	adult Adult algorithm Algorithms Area under the curves ATAC-seq Cell culture Child, Preschool chromatin Chromatin Chromatin accessibility Chromatin state assignment Chromosomes Clustering aggregation Conformal mapping Deep learning DNase-seq Enhancer Elements, Genetic enhancer region Enhancers Functional interpretations genetics HeLa cell line HeLa Cells human Humans Lanthanum compounds Learning methods Learning systems Learning techniques Machine learning Precision and recall preschool child promoter region Promoter Regions, Genetic Promoters Regulatory elements RPKM signal shape Self organizing maps Self-organizing maps State assignment Unsupervised method young adult Young Adult
Online Access:	View Fulltext in Publisher

Internet

View Fulltext in Publisher

Self-organizing maps with variable neighborhoods facilitate learning of chromatin accessibility signal shapes associated with regulatory elements

Internet

Similar Items