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Related Experiment Video

Updated: Jun 4, 2025

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

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

Published on: September 20, 2018

Annotating publicly-available samples and studies using interpretable modeling of unstructured metadata.

Hao Yuan^1,2, Parker Hicks³, Mansooreh Ahmadian⁴

¹Genetics and Genome Sciences Program, Michigan State University, East Lansing, MI 48823, United States.

Briefings in Bioinformatics

|December 22, 2024

Summary

This summary is machine-generated.

Txt2onto 2.0 enhances biomedical data reuse by annotating unstructured text to controlled vocabularies. This natural language processing method improves data findability and knowledge discovery.

Keywords:

biomedical metadata data reuse machine learning natural language processing

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Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

Area of Science:

Biomedical Informatics
Natural Language Processing
Machine Learning

Background:

Publicly available biomedical data is vast but difficult to reuse due to unstructured text descriptions.
Poor findability and reusability of biomedical data hinder scientific knowledge discovery.
Automated annotation of metadata is crucial for unlocking the potential of large biomedical datasets.

Purpose of the Study:

To introduce txt2onto 2.0, an improved method for annotating unstructured biomedical metadata to controlled vocabularies.
To enhance the interpretability and performance of biomedical text annotation, particularly with limited training data.
To demonstrate the generalizability of the method across different biomedical data types and sources.

Main Methods:

Txt2onto 2.0 utilizes natural language processing and machine learning, employing words as features for improved interpretability.
The method incorporates large language model embeddings to handle unseen words and explain annotations.
The approach was validated on independent datasets, including proteomics and clinical trial studies.

Main Results:

Txt2onto 2.0 shows improved performance and interpretability compared to its predecessor, txt2onto 1.0.
The method accurately predicts disease annotations for diverse biomedical studies.
The approach demonstrates generalizability across different experimental types and data sources.

Conclusions:

Txt2onto 2.0 offers a robust solution for annotating biomedical text, regardless of the data source or experimental context.
The method significantly improves the findability and reusability of public biomedical data.
This advancement facilitates greater knowledge discovery from massive biomedical datasets.