Studying stochastic systems biology of the cell with single-cell genomics data
Recent experimental developments in genome-wide RNA quantification hold considerable
promise for systems biology. However, rigorously probing the biology of living cells requires …
promise for systems biology. However, rigorously probing the biology of living cells requires …
Learning differential equation models from stochastic agent-based model simulations
JT Nardini, RE Baker… - Journal of the Royal …, 2021 - royalsocietypublishing.org
Agent-based models provide a flexible framework that is frequently used for modelling many
biological systems, including cell migration, molecular dynamics, ecology and epidemiology …
biological systems, including cell migration, molecular dynamics, ecology and epidemiology …
Biophysical modeling with variational autoencoders for bimodal, single-cell RNA sequencing data
Here we present biVI, which combines the variational autoencoder framework of scVI with
biophysical models describing the transcription and splicing kinetics of RNA molecules. We …
biophysical models describing the transcription and splicing kinetics of RNA molecules. We …
Quality variable prediction for nonlinear dynamic industrial processes based on temporal convolutional networks
Soft sensors have been extensively developed to estimate the difficult-to-measure quality
variables for real-time process monitoring and control. Process nonlinearities and dynamics …
variables for real-time process monitoring and control. Process nonlinearities and dynamics …
Quantifying how post-transcriptional noise and gene copy number variation bias transcriptional parameter inference from mRNA distributions
Transcriptional rates are often estimated by fitting the distribution of mature mRNA numbers
measured using smFISH (single molecule fluorescence in situ hybridization) with the …
measured using smFISH (single molecule fluorescence in situ hybridization) with the …
Neural-network solutions to stochastic reaction networks
The stochastic reaction network in which chemical species evolve through a set of reactions
is widely used to model stochastic processes in physics, chemistry and biology. To …
is widely used to model stochastic processes in physics, chemistry and biology. To …
Data-driven communication efficient distributed monitoring for multiunit industrial plant-wide processes
This study develops a novel data-driven latent variable correlation analysis (LVCA)
framework to achieve communication efficient distributed monitoring for industrial plant-wide …
framework to achieve communication efficient distributed monitoring for industrial plant-wide …
Prediction of gas concentration evolution with evolutionary attention-based temporal graph convolutional network
L Cheng, L Li, S Li, S Ran, Z Zhang, Y Zhang - Expert Systems with …, 2022 - Elsevier
Accurate prediction of gas concentration is of great importance in many safe-based systems
and applications. However, prediction accuracy of gas concentration is limited by not only …
and applications. However, prediction accuracy of gas concentration is limited by not only …
A novel dynamic model for the online prediction of rate of penetration and its industrial application to a drilling process
Accurate prediction of the rate of penetration (ROP) is a difficult issue in the drilling process,
especially under complex formation conditions. Many methods, such as mechanism and …
especially under complex formation conditions. Many methods, such as mechanism and …
Approximating solutions of the chemical master equation using neural networks
Summary The Chemical Master Equation (CME) provides an accurate description of
stochastic biochemical reaction networks in well-mixed conditions, but it cannot be solved …
stochastic biochemical reaction networks in well-mixed conditions, but it cannot be solved …