Measuring the State of the Art of Automated Pathway Curation Using Graph Algorithms - A Case Study of the mTOR Pathway
This work addresses the problem of automated pathway curation for bioinformatics researchers, but it is incremental as it focuses on quantifying existing gaps rather than introducing new methods.
The paper evaluated the gap between human-curated pathway maps and automated NLP systems by proposing graph analysis methods, using the mTOR pathway as a case study to quantify performance differences and identify areas for improvement.
This paper evaluates the difference between human pathway curation and current NLP systems. We propose graph analysis methods for quantifying the gap between human curated pathway maps and the output of state-of-the-art automatic NLP systems. Evaluation is performed on the popular mTOR pathway. Based on analyzing where current systems perform well and where they fail, we identify possible avenues for progress.