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Recommendations for the article Inferring Biological Networks by Sparse Identification of Nonlinear Dynamics
Abstract | Title | Authors | Publication Date | Journal/ Conference | Citation count | Highest h-index |
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visibility_off | Characterization of Biologically Relevant Network Structures form Time-series Data | Z. Tuza, G. Stan | 2018-09-24 | 2018 IEEE Conference on Decision and Control (CDC) | 3 | 36 |
visibility_off | Reconstruction of arbitrary biochemical reaction networks: A compressive sensing approach | W. Pan, Ye Yuan, G. Stan | 2012-05-08 | 2012 IEEE 51st IEEE Conference on Decision and Control (CDC) | 33 | 36 |
visibility_off | Inferring sparse networks for noisy transient processes | H. M. Tran, S. Bukkapatnam | 2016-02-26 | Scientific Reports | 6 | 35 |
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visibility_off | Koopman Operators for Generalized Persistence of Excitation Conditions for Nonlinear Systems | N. Boddupalli, A. Hasnain, S. Nandanoori, Enoch Yeung | 2019-06-25 | 2019 IEEE 58th Conference on Decision and Control (CDC) | 6 | 17 |
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visibility_off | Inverse problems in systems biology | H. Engl, Christoph Flamm, P. Kügler, James Lu, S. Müller, P. Schuster | 2009-12-01 | Inverse Problems | 140 | 48 |
visibility_off | Data-driven model discovery and model selection for noisy biological systems | Xiaojun Wu, MeiLu McDermott, Adam L. Maclean | 2024-10-04 | PLOS Computational Biology | 3 | 19 |
visibility_off | Data-driven discovery of dynamical models in biology | Bartosz Prokop, L. Gelens | 2025-09-08 | ArXiv | 0 | 32 |
Abstract | Title | Authors | Publication Date | Journal/Conference | Citation count | Highest h-index |