Cargando…
FITs: forest of imputation trees for recovering true signals in single-cell open chromatin profiles
The advent of single-cell open-chromatin profiling technology has facilitated the analysis of heterogeneity of activity of regulatory regions at single-cell resolution. However, stochasticity and availability of low amount of relevant DNA, cause high drop-out rate and noise in single-cell open-chrom...
Autores principales: | Sharma, Rachesh, Pandey, Neetesh, Mongia, Aanchal, Mishra, Shreya, Majumdar, Angshul, Kumar, Vibhor |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7676476/ https://www.ncbi.nlm.nih.gov/pubmed/33575635 http://dx.doi.org/10.1093/nargab/lqaa091 |
Ejemplares similares
-
McImpute: Matrix Completion Based Imputation for Single Cell RNA-seq Data
por: Mongia, Aanchal, et al.
Publicado: (2019) -
AutoImpute: Autoencoder based imputation of single-cell RNA-seq data
por: Talwar, Divyanshu, et al.
Publicado: (2018) -
Drug-target interaction prediction using Multi Graph Regularized Nuclear Norm Minimization
por: Mongia, Aanchal, et al.
Publicado: (2020) -
Improving Chromatin-Interaction Prediction Using Single-Cell Open-Chromatin Profiles and Making Insight Into the Cis-Regulatory Landscape of the Human Brain
por: Pandey, Neetesh, et al.
Publicado: (2021) -
A computational approach to aid clinicians in selecting anti-viral drugs for COVID-19 trials
por: Mongia, Aanchal, et al.
Publicado: (2021)