Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Genetic Screens

Genetic Screens

Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which result in visible changes...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Improving hit discovery by integrating activity cliff sensitivity into active learning.

Bioinformatics (Oxford, England)·2026

Same author

Knowledge-guided contextual gene set analysis with large language models.

Bioinformatics (Oxford, England)·2026

Same author

Large Language Models Meet Biomedical Knowledge Graphs for Mechanistically Grounded Therapeutic Prioritization.

ArXiv·2026

Same author

DeepER-Med: Advancing Deep Evidence-Based Research in Medicine Through Agentic AI.

ArXiv·2026

Same author

MedHopQA: A Disease-Centered Multi-Hop Reasoning Benchmark and Evaluation Framework for LLM-Based Biomedical Question Answering.

ArXiv·2026

Same author

Validation of a 2-Gene Blood Test for Kawasaki Disease in Febrile Children.

JAMA network open·2026

Search research articles

Related Experiment Video

Updated: May 16, 2026

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions

Published on: May 27, 2021

Prioritizing PubMed articles for the Comparative Toxicogenomic Database utilizing semantic information.

Sun Kim¹, Won Kim, Chih-Hsuan Wei

¹National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA.

Database : the Journal of Biological Databases and Curation

|November 20, 2012

Summary

This study introduces a machine learning framework to efficiently identify relevant scientific articles for the Comparative Toxicogenomics Database (CTD). The system improves manual curation by prioritizing articles on chemical-gene, chemical-disease, and gene-disease relationships.

More Related Videos

Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation

Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation

Published on: February 10, 2023

Related Experiment Videos

Last Updated: May 16, 2026

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions

Published on: May 27, 2021

Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation

Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation

Published on: February 10, 2023

Area of Science:

Bioinformatics
Computational Biology
Toxicogenomics

Background:

The Comparative Toxicogenomics Database (CTD) manually curates literature on chemical-gene, chemical-disease, and gene-disease relationships.
Efficiently identifying relevant articles is crucial for manual curation but challenging due to complex entities and relationships.

Purpose of the Study:

To develop a machine learning framework for prioritizing articles relevant to the CTD.
To enhance the efficiency of manual curation for toxicogenomic data.

Main Methods:

A machine learning framework was adapted from a prior protein-protein interaction article classification system.
A novel entity identification method for genes, chemicals, and diseases was explored.
Latent topic analysis was used as a feature type to address small training set sizes.

Main Results:

The system achieved a mean average precision (MAP) of 0.8030 on the BioCreative 2012 Triage dataset.
The proposed method ranked as the top MAP system among participants.
The system received positive feedback from the CTD curation team when integrated with PubTator.

Conclusions:

The developed machine learning framework effectively prioritizes CTD-relevant articles.
The novel entity identification and latent topic features improve performance, especially with limited training data.
The system shows promise for enhancing the efficiency of biomedical literature curation.