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To interpret the genetic profile present in a patient sample, it is necessaryto know which mutations have important roles in the development of thecorresponding cancer type. Named entity recognition is a core step in the textmining pipeline which facilitates mining valuable cancer information from thescientific literature. However, due to the scarcity of related datasets,previous NER attempts in this domain either suffer from low performance whendeep learning based models are deployed, or they apply feature based machinelearning models or rule based models to tackle this problem, which requiresintensive efforts from domain experts, and limit the model generalizationcapability. In this paper, we propose EPICURE, an ensemble pre trained modelequipped with a conditional random field pattern layer and a span predictionpattern layer to extract cancer mutations from text. We also adopt a dataaugmentation strategy to expand our training set from multiple datasets.Experimental results on three benchmark datasets show competitive resultscompared to the baseline models.

EPICURE Ensemble Pretrained Models for Extracting Cancer Mutations from Literature