Cargando…
Data-driven mechanistic analysis method to reveal dynamically evolving regulatory networks
Motivation: Mechanistic models based on ordinary differential equations provide powerful and accurate means to describe the dynamics of molecular machinery which orchestrates gene regulation. When combined with appropriate statistical techniques, mechanistic models can be calibrated using experiment...
Autores principales: | Intosalmi, Jukka, Nousiainen, Kari, Ahlfors, Helena, Lähdesmäki, Harri |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2016
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4908358/ https://www.ncbi.nlm.nih.gov/pubmed/27307629 http://dx.doi.org/10.1093/bioinformatics/btw274 |
Ejemplares similares
-
A network-driven approach for genome-wide association mapping
por: Lee, Seunghak, et al.
Publicado: (2016) -
Comparative analyses of population-scale phenomic data in electronic medical records reveal race-specific disease networks
por: Glicksberg, Benjamin S., et al.
Publicado: (2016) -
What time is it? Deep learning approaches for circadian rhythms
por: Agostinelli, Forest, et al.
Publicado: (2016) -
DrugE-Rank: improving drug–target interaction prediction of new candidate drugs or targets by ensemble learning to rank
por: Yuan, Qingjun, et al.
Publicado: (2016) -
A convex optimization approach for identification of human tissue-specific interactomes
por: Mohammadi, Shahin, et al.
Publicado: (2016)