Unsupervised logic-based mechanism inference for network-driven biological processes.
Modern analytical techniques enable researchers to collect data about cellular states, before and after perturbations. These states can be characterized using analytical techniques, but the inference of regulatory interactions that explain and predict changes in these states remains a challenge. Her...
Guardado en:
Autores principales: | Martina Prugger, Lukas Einkemmer, Samantha P Beik, Perry T Wasdin, Leonard A Harris, Carlos F Lopez |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Public Library of Science (PLoS)
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/e3bf50ee75e745d68f3898b25e571aef |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
A Novel Unsupervised Algorithm for Biological Process-based Analysis on Cancer
por: Tianci Song, et al.
Publicado: (2017) -
The Convallis rule for unsupervised learning in cortical networks.
por: Pierre Yger, et al.
Publicado: (2013) -
Approaches for unsupervised identification of data-driven models for flow forecasting in urban drainage systems
por: Ari Jóhannesson, et al.
Publicado: (2021) -
Perturbation biology: inferring signaling networks in cellular systems.
por: Evan J Molinelli, et al.
Publicado: (2013) -
Circuits with broken fibration symmetries perform core logic computations in biological networks.
por: Ian Leifer, et al.
Publicado: (2020)