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

Kewei Li; Yusi Fan; Yaqing Liu; Hongmei Liu; Gongyou Zhang; Meiyu Duan; Lan Huang; Fengfeng Zhou

doi:10.3791/66030

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

JoVE Journal
Biology

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JoVE Journal Biology

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

614 Views

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03:37 min

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March 01, 2024

DOI:

10.3791/66030-v

DOI:

10.3791/66030-v

•

03:37 min

March 01, 2024

•

1 Views

Kewei Li¹, Yusi Fan¹, Yaqing Liu¹, Hongmei Liu², Gongyou Zhang², Meiyu Duan¹, Lan Huang¹, Fengfeng Zhou¹

¹College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education,Jilin University, ²School of Biology and Engineering,Guizhou Medical University

Transcript

Our research focuses on the intergenetic interactions on disease. We found that many genes have complex, intertwined relationships with each other. By exploring these relationships, we aim in making more precise diagnosis in disease treatment and management.

We found that many dark biomarkers which traditional, conventional methods ignored, unsupported by many medical literatures. This indicates that approach can further assist biologists in reducing time cycle for biomarker screening. The biggest challenge during the experiment was addressing the issue of small sample sizes of different disease types.

To overcome this difficulty, we collected as many healthy samples as possible to train a reference model, which we called health model.

Summary

Here, we introduce a protocol for converting transcriptomic data into a mqTrans view, enabling the identification of dark biomarkers. While not differentially expressed in conventional transcriptomic analyses, these biomarkers exhibit differential expression in the mqTrans view. The approach serves as a complementary technique to traditional methods, unveiling previously overlooked biomarkers.