Cargando…
Predicting unrecognized enhancer-mediated genome topology by an ensemble machine learning model
Transcriptional enhancers commonly work over long genomic distances to precisely regulate spatiotemporal gene expression patterns. Dissecting the promoters physically contacted by these distal regulatory elements is essential for understanding developmental processes as well as the role of disease-a...
Autores principales: | Tang, Li, Hill, Matthew C., Wang, Jun, Wang, Jianxin, Martin, James F., Li, Min |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cold Spring Harbor Laboratory Press
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7706734/ https://www.ncbi.nlm.nih.gov/pubmed/33184104 http://dx.doi.org/10.1101/gr.264606.120 |
Ejemplares similares
-
Ensemble machine learning modeling for the prediction of artemisinin resistance in malaria
por: Ford, Colby T., et al.
Publicado: (2020) -
DeepProg: an ensemble of deep-learning and machine-learning models for prognosis prediction using multi-omics data
por: Poirion, Olivier B., et al.
Publicado: (2021) -
LightGBM: accelerated genomically designed crop breeding through ensemble learning
por: Yan, Jun, et al.
Publicado: (2021) -
EnsembleCNV: an ensemble machine learning algorithm to identify and genotype copy number variation using SNP array data
por: Zhang, Zhongyang, et al.
Publicado: (2019) -
Genome-wide prediction of pathogenic gain- and loss-of-function variants from ensemble learning of a diverse feature set
por: Stein, David, et al.
Publicado: (2023)