CLARINET

CLARINET (CLARIfying NETworks) is a novel tool for rapid model assembly by automatically extending dynamic network models with the information published in literature. This facilitate information reuse and data reproducibility and replaces hundred of thousands of manual experiments, thereby reducing the time needed for the advancement of knowledge. The objectives of CLARINET are:

  1. Utilizing the knowledge published in literature and suggests model extensions
  2. Studying events extracted from literature as a collaboration graph, including several metrics that rely on the event occurrence and co-occurrence frequency in literature
  3. Allowing users to explore different selectrion criteria when automatically finding best extensions for their models

Documentation

Documentation for CLARINET can be found at Documentation Status

Tutorial

An interactive tutorial notebook to use CLARINET can be accessed via Binder

Flow Diagram

Citation

To use and cite the CLARINET tool, please use the following:

  • Yasmine Ahmed, Cheryl A Telmer, Natasa Miskov-Zivanov, CLARINET: efficient learning of dynamic network models from literature, Bioinformatics Advances, Volume 1, Issue 1, 2021, vbab006, https://doi.org/10.1093/bioadv/vbab006

Related Publications

  • Yasmine Ahmed, Adam A Butchy, Khaled Sayed, Cheryl Telmer, Natasa Miskov-Zivanov, “New advances in the automation of context-aware information selection and guided model assembly”, arXiv preprint arXiv:2110.10841, Accepted at the 13th International Workshop on Bio-Design Automation (IWBDA21).
  • Yasmine Ahmed, Natasa Miskov-Zivanov, “Guided assembly of cellular network models from knowledge in literature”, arXiv preprint arXiv:2110.04468, To appear in the 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC2021).