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Constructing and Visualizing Models using Mime-based Machine-learning Framework

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Published on: July 22, 2025

Combining Literature Mining and Machine Learning for Predicting Biomedical Discoveries.

Balu Bhasuran^1,2

¹DRDO-BU Center for Life Sciences, Bharathiar University Campus, Coimbatore, Tamilnadu, India. balubhasuran08@gmail.com.

Methods in Molecular Biology (Clifton, N.J.)

|June 17, 2022

Summary

This summary is machine-generated.

Biomedical literature mining helps researchers discover gene-disease relationships. This approach uses machine learning and text mining tools to accelerate biomarker discovery and advance precision medicine.

Keywords:

ABC Principle BCC NER DNER Gene–Disease Association Literature Based Discovery Machine Learning Neural Networks

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

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Published on: June 13, 2025

Area of Science:

Biomedical Informatics
Computational Biology
Genomics

Background:

The exponential growth of biomedical literature presents challenges for researchers in extracting critical information.
Traditional empirical methods for identifying disease-causing genes and validating them are time-consuming and costly.
Biomedical literature mining offers an automated solution for knowledge extraction and discovery.

Purpose of the Study:

To present a protocol for combining literature mining and machine learning to predict biomedical discoveries, focusing on gene-disease relationships.
To introduce a Literature-Based Discovery (LBD) pipeline tailored for gene-disease association.
To demonstrate the utility of in silico approaches for accelerating biomarker discovery.

Main Methods:

Development of a Literature-Based Discovery (LBD) pipeline.
Utilizing named entity recognition tools (DNER, BCCNER) for disease and gene/protein identification.
Employing a novel deep learning method for association discovery and statistically validated relation extraction (DisGeReExT).

Main Results:

The proposed protocol effectively integrates literature mining and machine learning for gene-disease relation discovery.
The developed web-based tools facilitate automated entity recognition and relation extraction.
The deep learning approach shows promise for generalization to other biomedical discovery tasks.

Conclusions:

The LBD pipeline and associated tools provide an efficient in silico method for identifying gene-disease associations.
This approach can significantly accelerate the discovery of biomarkers and support personalized medicine and drug repurposing efforts.
The methodology is adaptable for discovering other biomedical entities like drugs, chemicals, or microRNAs.