FLUTE

Understanding disease at the cellular level requires detailed knowledge of signaling networks. To aid in this task, many advances have been made in the field of natural language processing (NLP) to extract signaling events from biomedical literature. However, even state-of-the-art NLP methods incorrectly interpret some signaling events described in the literature. The FiLter for Understanding True Events (FLUTE) tool seeks to identify high-confidence signaling events from biomedical NLP output by comparing with existing biological databases. As such, FLUTE can reliably determine the confidence in the biomolecular events extracted by NLP methods and at the same time provide a speedup in event filtering by three orders of magnitude.”

Documentation

Documentation for FLUTE can be found at Documentation Status

Description

 

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FLUTE uses available databases to filter extracted interactions obtained as output of knowledge extraction process from available literature, which is usually initiated by user queries. The selected interactions from FLUTE are inputs to model assembly that creates models, which are then explored with model analysis in order to provide answers to user questions.

Citation

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

  • E. Holtzapple, C.A. Telmer, N. Miskov-Zivanov,  "FLUTE: Fast and reliable knowledge retrieval from biomedical literature," Database, Volume 2020, 2020, baaa056, https://doi.org/10.1093/database/baaa056

Related Publications

  • E. Holtzapple, B. Cochran, and N. Miskov-Zivanov, “Automated verification, assembly, and extension of GBM stem cell network model with knowledge from literature and data,” Systems Biology, preprint, Jul. 2021. doi: 10.1101/2021.07.04.451062.
  • E. Holtzapple, B. Cochran, and N. Miskov-Zivanov, “Context-aware query design combines knowledge and data for efficient reading and reasoning,” in Proceedings of the 20th Workshop on Biomedical Language Processing, Online, 2021, pp. 238–246. doi: 10.18653/v1/2021.bionlp-1.26.