How to deal with parameters for whole-cell modelling
Dynamical systems describing whole cells are on the verge of becoming a reality. But as
models of reality, they are only useful if we have realistic parameters for the molecular …
models of reality, they are only useful if we have realistic parameters for the molecular …
Causal deep learning: encouraging impact on real-world problems through causality
Causality has the potential to truly transform the way we solve a large number of real-world
problems. Yet, so far, its potential largely remains to be unlocked as causality often requires …
problems. Yet, so far, its potential largely remains to be unlocked as causality often requires …
Inferring biological networks by sparse identification of nonlinear dynamics
Inferring the structure and dynamics of network models is critical to understanding the
functionality and control of complex systems, such as metabolic and regulatory biological …
functionality and control of complex systems, such as metabolic and regulatory biological …
Causal deep learning
J Berrevoets, K Kacprzyk, Z Qian… - ar** informative and realistic models of such systems typically involves …
NSCGRN: a network structure control method for gene regulatory network inference
W Liu, X Sun, L Yang, K Li, Y Yang… - Briefings in …, 2022 - academic.oup.com
Accurate inference of gene regulatory networks (GRNs) is an essential premise for
understanding pathogenesis and curing diseases. Various computational methods have …
understanding pathogenesis and curing diseases. Various computational methods have …
Network-based approaches for analysis of complex biological systems
Cells function and respond to changes in their environment by the coordinated activity of
their molecular components, including mRNAs, proteins and metabolites. At the heart of …
their molecular components, including mRNAs, proteins and metabolites. At the heart of …