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Improving Translational Accuracy

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

Improving Translational Accuracy

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

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Published on: September 20, 2018

Improving biomedical Named Entity Recognition with additional external contexts.

Bui Duc Tho¹, Minh-Tien Nguyen², Dung Tien Le³

¹Hung Yen University of Technology and Education, Viet Nam; University of Engineering and Technology, Vietnam National University, Hanoi, Viet Nam.

Journal of Biomedical Informatics

|June 13, 2024

Summary

This summary is machine-generated.

This study introduces a novel model for biomedical named entity recognition (bio NER) that leverages external context from PubMed. The enhanced model significantly improves entity recognition accuracy by incorporating relevant contextual information.

Keywords:

Biomedical Named Entity Recognition External contexts Information extraction Transformers

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Area of Science:

Biomedical Informatics
Natural Language Processing
Computational Biology

Background:

Biomedical Named Entity Recognition (bio NER) is crucial for extracting information from biomedical texts.
Existing bio NER models primarily rely on original input sequences, potentially limiting entity representation.
External contextual information can enhance the understanding and recognition of biomedical entities.

Purpose of the Study:

To develop and evaluate a novel bio NER model that incorporates additional external contexts.
To improve the accuracy and robustness of biomedical named entity recognition.
To demonstrate the benefit of using relevant external information for bio NER.

Main Methods:

Retrieving and ranking relevant sentences from PubMed to form external contexts.
Combining original input sequences with retrieved contexts to create enhanced sequences.
Utilizing PubMedBERT for feature representation, followed by BiLSTM and CRF layers for sequence labeling.
End-to-end joint training to optimize the model's performance with additional contexts.

Main Results:

The proposed model achieved promising performance on six biomedical datasets.
Experimental results demonstrated the effectiveness of incorporating additional contexts for bio NER.
The model outperformed strong baseline methods in biomedical named entity recognition tasks.

Conclusions:

Additional context from PubMed significantly improves the quality of biomedical entity recognition.
PubMed serves as a more appropriate source for bio NER context than general search engines.
Relevant contextual sentences are more beneficial than irrelevant ones for enhancing entity recognition.