Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data

We present a systematic evaluation of state-of-the-art algorithms for inferring gene regulatory networks (GRNs) from single-cell transcriptional data. As the ground truth for assessing accuracy, we use synthetic networks with predictable trajectories, literature-curated Boolean models, and diverse t...

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Detalles Bibliográficos
Autores principales: Pratapa, Aditya, Jalihal, Amogh P., Law, Jeffrey N., Bharadwaj, Aditya, Murali, T. M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7098173/
https://www.ncbi.nlm.nih.gov/pubmed/31907445
http://dx.doi.org/10.1038/s41592-019-0690-6

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7098173/
https://www.ncbi.nlm.nih.gov/pubmed/31907445
http://dx.doi.org/10.1038/s41592-019-0690-6

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