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20122018
most citedReconstruction of Arbitrary Biochemical Reaction Networks: A Compressive Sensing Approach

25 citations · 50 across the 9 of their papers we have counts for

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6 papers · 1 filter

eess.SY2017★ 5 cited

Distributed Kalman filtering with minimum-time consensus algorithm

Ye Yuan, Ling Shi, Jun Liu +3

Fueled by applications in sensor networks, these years have witnessed a surge of interest in distributed estimation and filtering. A new approach is hereby proposed for the Distrib…

eess.SY2015★ 2 cited

Identifying Biochemical Reaction Networks From Heterogeneous Datasets

Wei Pan, Ye Yuan, Lennart Ljung +2

In this paper, we propose a new method to identify biochemical reaction networks (i.e. both reactions and kinetic parameters) from heterogeneous datasets. Such datasets can contain…

eess.SY2014

On minimal realisations of dynamical structure functions

Ye Yuan, Keith Glover, Jorge Goncalvees

Motivated by the fact that transfer functions do not contain structural information about networks, dynamical structure functions were introduced to capture causal relationships be…

eess.SY2013★ 5 cited

Network Reconstruction from Intrinsic Noise

David Hayden, Ye Yuan, Jorge Goncalves

This paper considers the problem of inferring an unknown network of dynamical systems driven by unknown, intrinsic, noise inputs. Equivalently we seek to identify direct causal dep…

eess.SY2012★ 2 cited

Minimal realization of the dynamical structure function and its application to network reconstruction

Ye Yuan, Guy-Bart Stan, Sean Warnick +1

Network reconstruction, i.e., obtaining network structure from data, is a central theme in systems biology, economics and engineering. In some previous work, we introduced dynamica…

eess.SY2012★ 25 cited

Reconstruction of Arbitrary Biochemical Reaction Networks: A Compressive Sensing Approach

Wei Pan, Ye Yuan, Guy-Bart Stan

Reconstruction of biochemical reaction networks is a central topic in systems biology which raises crucial theoretical challenges in system identification. Nonlinear Ordinary Diffe…