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Related Experiment Video

Updated: Jun 2, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Using unsupervised patterns to extract gene regulation relationships for network construction.

Yi-Tsung Tang¹, Shuo-Jang Li, Hung-Yu Kao

¹Department of Computer Science and Information Engineering, National Cheng Kung Tainan, Taiwan, Republic of China. p7895125@mail.ncku.edu.tw

|May 17, 2011

Summary

This summary is machine-generated.

AutoPat, an unsupervised gene expression mining system, automatically generates patterns from seed patterns. This approach enhances the precision and effectiveness of extracting gene expression data from biomedical literature.

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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Published on: March 1, 2024

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Biomedical literature describes gene expression as transcription factor regulation of target genes.
Previous gene regulation discovery methods relied on manual annotation, which is time-consuming and difficult to scale.
The rapid growth of biomedical text necessitates automated pattern discovery methods.

Purpose of the Study:

To develop an automated system for generating gene expression patterns.
To overcome the limitations of manual pattern creation in biomedical literature analysis.

Main Methods:

Introduced AutoPat, an unsupervised pattern generation method.
AutoPat generates unsupervised patterns automatically from seed patterns.
The system is designed for high scalability and low maintenance.

Main Results:

AutoPat enables precise and effective extraction of gene expression from PubMed abstracts.
Unsupervised patterns offer high scalability and low maintenance costs.
Experimental validation demonstrated reasonable precision and recall rates.

Conclusions:

AutoPat offers a practical and effective solution for automated gene expression pattern discovery.
The system facilitates the precise and effective construction of gene regulation networks.
The AutoPat system is publicly available for use.