StochNetV2: A Tool for Automated Deep Abstractions for Stochastic Reaction Networks
| Autor: | Tatjana Petrov, Denis Repin, Nhat-Huy Phung |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0303 health sciences
Artificial neural network Computer science business.industry Stochastic process Deep learning Distributed computing Markov process 02 engineering and technology Abstract process 03 medical and health sciences symbols.namesake Application domain Kernel (statistics) 0202 electrical engineering electronic engineering information engineering symbols Mixture distribution 020201 artificial intelligence & image processing Artificial intelligence business 030304 developmental biology |
| Zdroj: | Quantitative Evaluation of Systems ISBN: 9783030598532 QEST |
| Popis: | We present a toolbox for stochastic simulations with CRN models and their (automated) deep abstractions: a mixture density deep neural network trained on time-series data produced by the CRN. The optimal neural network architecture is learnt along with learning the transition kernel of the abstract process. Automated search of the architecture makes the method applicable directly to any given CRN, which is time-saving for deep learning experts and crucial for non-specialists. The tool was primarily designed to efficiently reproduce simulation traces of given complex stochastic reaction networks arising in systems biology research, possibly with multi-modal emergent phenotypes. It is at the same time applicable to any other application domain, where time-series measurements of a Markovian stochastic process are available by experiment or synthesised with simulation (e.g. are obtained from a rule-based description of the CRN). |
| Databáze: | OpenAIRE |
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