Article

Supervised learning on synthetic data for reverse engineering gene regulatory networks from experimental time-series

2018-06-27

Abstract excerpt

The reconstruction of gene regulatory networks from time resolved gene expression measurements is a key challenge in systems biology with applications in health and disease. While the most popular network inference methods are based on unsupervised learning approaches, supervised learning methods have proven their potential for superior reconstruction performance. However, obtaining the appropriate volume of infor...

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Literature Corpus work: afafd361-d0df-590e-8360-4ce35f9103e5
DOI: 10.1101/356477

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