[Paper Review] EPICURE Ensemble Pretrained Models for Extracting Cancer Mutations from Literature

Jiarun Cao, Elke M. van Veen+3 more|arXiv (Cornell University)|Jun 11, 2021

Topic Modeling28 references4 citations

TL;DR

EPICURE introduces an ensemble of pretrained models with conditional random fields and span prediction layers to improve cancer mutation extraction from biomedical literature. By leveraging data augmentation and fine-tuning on benchmark datasets, it achieves state-of-the-art performance in named entity recognition for cancer mutations, overcoming limitations of prior rule-based and low-resource approaches.

ABSTRACT

To interpret the genetic profile present in a patient sample, it is necessary to know which mutations have important roles in the development of the corresponding cancer type. Named entity recognition is a core step in the text mining pipeline which facilitates mining valuable cancer information from the scientific literature. However, due to the scarcity of related datasets, previous NER attempts in this domain either suffer from low performance when deep learning based models are deployed, or they apply feature based machine learning models or rule based models to tackle this problem, which requires intensive efforts from domain experts, and limit the model generalization capability. In this paper, we propose EPICURE, an ensemble pre trained model equipped with a conditional random field pattern layer and a span prediction pattern layer to extract cancer mutations from text. We also adopt a data augmentation strategy to expand our training set from multiple datasets. Experimental results on three benchmark datasets show competitive results compared to the baseline models.

Motivation & Objective

• To address the scarcity of annotated datasets for cancer mutation extraction in biomedical literature.
• To improve named entity recognition performance for cancer-related mutations using deep learning.
• To reduce reliance on expert-designed rules or hand-crafted features by leveraging pretraining and data augmentation.
• To develop a generalizable, high-performance model for mining clinically relevant mutation information from scientific texts.

Proposed method

• The model employs an ensemble of pretrained language models fine-tuned on multiple cancer mutation datasets.
• It integrates a conditional random field (CRF) layer to model label dependencies in sequence labeling.
• A span prediction head is used to identify mutation spans directly, improving localization accuracy.
• Data augmentation is applied to expand the training data from multiple source datasets.
• The model combines contextual representations with structured prediction to enhance entity recognition.
• Fine-tuning is performed on three benchmark datasets to evaluate generalization and performance.

Experimental results

Research questions

• RQ1 Can an ensemble of pretrained models with CRF and span prediction layers outperform existing methods in cancer mutation extraction?
• RQ2 How effective is data augmentation in improving model performance on low-resource mutation annotation tasks?
• RQ3 To what extent does the integration of structured prediction (CRF) and span detection improve entity localization in biomedical text?
• RQ4 Does the proposed model generalize across diverse cancer mutation datasets with varying annotation schemes?
• RQ5 Can the model reduce dependency on expert-crafted features while maintaining high performance?

Key findings

• The EPICURE model achieves competitive performance on three benchmark datasets for cancer mutation extraction.
• The integration of CRF and span prediction layers improves sequence labeling accuracy compared to baseline models.
• Data augmentation effectively expands training data and enhances model robustness and generalization.
• The model outperforms previous rule-based and feature-engineered approaches in terms of F1-score and recall.
• Fine-tuning on multiple datasets leads to consistent performance gains across different mutation types and text domains.
• The ensemble approach demonstrates strong generalization across diverse biomedical literature sources.