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Titlebook: Computational Methods in Systems Biology; 15th International C Jér?me Feret,Heinz Koeppl Conference proceedings 2017 Springer International

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41#
發(fā)表于 2025-3-28 17:58:18 | 只看該作者
Computational Methods in Systems Biology978-3-319-67471-1Series ISSN 0302-9743 Series E-ISSN 1611-3349
42#
發(fā)表于 2025-3-28 19:22:41 | 只看該作者
Xinghang Dai,Nada Matta,Guillaume Ducellierresented, based on rewriting in hierarchies of graphs, together with a specific instantiation of the methodology that addresses our particular bio-curation problem. The current state of the ongoing development of the . (.nowledge .ggregator & .odel .nstantiator) bio-curation tool, based on this appr
43#
發(fā)表于 2025-3-29 01:10:19 | 只看該作者
44#
發(fā)表于 2025-3-29 05:43:36 | 只看該作者
https://doi.org/10.1007/978-3-642-16358-6ermine these attractors. Biological systems are usually described by highly parametrised dynamical models that can be represented as parametrised graphs typically constructed as discrete abstractions of continuous-time models. In such models, attractors are observed in the form of terminal strongly
45#
發(fā)表于 2025-3-29 08:03:56 | 只看該作者
46#
發(fā)表于 2025-3-29 13:45:47 | 只看該作者
https://doi.org/10.1007/978-3-031-43666-6ver, despite the spectacular progress of machine learning techniques in data analytics, classification, clustering and prediction making, learning dynamical models from data time-series is still challenging. In this paper we investigate the use of the Probably Approximately Correct (PAC) learning fr
47#
發(fā)表于 2025-3-29 16:56:08 | 只看該作者
48#
發(fā)表于 2025-3-29 19:44:51 | 只看該作者
49#
發(fā)表于 2025-3-30 03:18:21 | 只看該作者
50#
發(fā)表于 2025-3-30 06:25:35 | 只看該作者
Felix Schulze,Patrick Dallasegaaccessible. However, reproducing published results, either experimental data or observations is often not viable. In this work, we propose a framework to overcome some of the issues of reproducing previous research, and to ensure re-usability of published information. We present here a framework tha
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