MTOR-NLP
Cross-source consensus on MTOR-NLP from 1 sources and 5 claims.
1 sources · 5 claims
Preparation
Comparisons
Where it comes from
Highlighted claims
- MTOR-NLP was derived from the papers cited by the curated mTOR map, with text successfully extracted from 501 papers. — Measuring the State of the Art of Automated Pathway Curation Using Graph Algorithms - A Case Study of the mTOR Pathway
- MTOR-NLP used TEES to process full-text papers and convert extracted entities and events into SBML pathway representations. — Measuring the State of the Art of Automated Pathway Curation Using Graph Algorithms - A Case Study of the mTOR Pathway
- MTOR-NLP produced far more species-like strings than the human-curated pathway retained. — Measuring the State of the Art of Automated Pathway Curation Using Graph Algorithms - A Case Study of the mTOR Pathway
- MTOR-NLP's exact species-name precision was below 1%, yet those exact matches still covered 45.88% of curated species. — Measuring the State of the Art of Automated Pathway Curation Using Graph Algorithms - A Case Study of the mTOR Pathway
- MTOR-NLP recovered substantially more of the curated graph under the most relaxed matching strategy than under strict matching. — Measuring the State of the Art of Automated Pathway Curation Using Graph Algorithms - A Case Study of the mTOR Pathway