Neural network aided approximation and parameter inference of non-Markovian models of gene expression

Autor: Qingchao Jiang, Xiaoming Fu, Shifu Yan, Runlai Li, Wenli Du, Zhixing Cao, Feng Qian, Ramon Grima

Publikováno v: Nature Communications, Vol 12, Iss 1, Pp 1-12 (2021)

Cells are complex systems that make decisions biologists struggle to understand. Here, the authors use neural networks to approximate the solution of mathematical models that capture the history and randomness of biochemical processes in order to und

Externí odkaz: https://doaj.org/article/4bc73034fff748fc9da54f683d9b7be8

Learning Output Relevant Features by Joint Autoencoder

Autor: Shifu Yan, Xuefeng Yan

Publikováno v: IEEE Transactions on Cybernetics. :1-9

Classical regression is a supervised task that uses target output to guide the modeling. Generally, the original input contains both output relevant and irrelevant information, whereas the latter may decrease the predictive performance to a certain e

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8cd634f13e19ff0bc9250e44c2764cfa
https://doi.org/10.1109/tcyb.2022.3185588

Neural network aided approximation and parameter inference of non-Markovian models of gene expression

Autor: Ramon Grima, Xiaoming Fu, Runlai Li, Feng Qian, Zhixing Cao, Shifu Yan, Wenli Du, Qingchao Jiang

Publikováno v: Nature Communications, Vol 12, Iss 1, Pp 1-12 (2021)
Jiang, Q, Fu, X, Yan, S, Li, R, Du, W, Cao, Z, Feng, Q & Grima, R 2021, ' Neural network aided approximation and parameter inference of non-Markovian models of gene expression ', Nature Communications, vol. 12, 2618 . https://doi.org/10.1038/s41467-021-22919-1

Non-Markovian models of stochastic biochemical kinetics often incorporate explicit time delays to effectively model large numbers of intermediate biochemical processes. Analysis and simulation of these models, as well as the inference of their parame

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::942996c29a8624de8e00b60aefb03e2a
https://doaj.org/article/4bc73034fff748fc9da54f683d9b7be8

Nonlinear quality-relevant process monitoring based on maximizing correlation neural network

Autor: Xuefeng Yan, Shifu Yan

Publikováno v: Neural Computing and Applications. 33:10129-10139

Quality-relevant fault detection aims to reveal whether quality variables are affected when a fault is detected. For current industrial processes, kernel-based methods focus on the nonlinearity within process variables, which is insufficient for obta

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::ecd2ef4c97136a71e91da57a4309288a
https://doi.org/10.1007/s00521-021-05776-3

Quality-Driven Autoencoder for Nonlinear Quality-Related and Process-Related Fault Detection Based on Least-Squares Regularization and Enhanced Statistics

Autor: Xuefeng Yan, Shifu Yan

Publikováno v: Industrial & Engineering Chemistry Research. 59:12136-12143

Although many kernel-based quality-related monitoring methods have been developed for nonlinear processes, the nonlinearity between process variables and quality indicators is not well interpreted ...

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::8fae43d57655f8f149ce1530a279574d
https://doi.org/10.1021/acs.iecr.0c00735