ACCORDION

ACCORDION (ACCelerating and Optimizing model RecommenDatIONs) is novel tool and methodology for rapid model assembly by automatically extending dynamic network models with the information published in literature. The objectives of ACCORDION are:

  1. Extending dynamic network models by combining clustering and path finding with model testing on a set of predefined desired system properties
  2. Evaluating the effect of published literature and machine reading when automatically reconstructing an existing model

Documentation

Documentation for ACCORDION can be found at Documentation Status

Tutorial

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

Flow Diagram

Citation

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

  • Yasmine Ahmed, Cheryl Telmer, Gaoxiang Zhou, Natasa Miskov-Zivanov, “Context-aware knowledge selection and reliable model recommendation with ACCORDION”, bioRxiv preprint, doi: https://doi.org/10.1101/2022.01.22.477231.

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).
  • Y. Ahmed, C. Telmer, and N. Miskov-Zivanov, “ACCORDION: Clustering and Selecting Relevant Data for Guided Network Extension and Query Answering,” arXiv:2002.05748 [q-bio], May 2020, Accessed: Jun. 01, 2021. Available: http://arxiv.org/abs/2002.05748