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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models

Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models

Published on: December 9, 2016

RDRGSE: A Framework for Noncoding RNA-Drug Resistance Discovery by Incorporating Graph Skeleton Extraction and

Ping Zhang¹, Zilin Wang¹, Weicheng Sun¹

¹Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.

|August 7, 2023

Summary

This study introduces RDRGSE, a computational framework that accurately predicts noncoding RNA (ncRNA)-drug resistance associations. The method enhances understanding of ncRNA function and aids in identifying drug resistance biomarkers.

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Area of Science:

Computational biology
Genomics
Bioinformatics

Background:

Identifying noncoding RNA (ncRNA)-drug resistance associations computationally aids in understanding molecular mechanisms and reduces experimental costs.
Existing graph neural network methods face challenges due to noisy data in predicting these associations.

Purpose of the Study:

To develop a robust computational framework, RDRGSE, for accurate prediction of ncRNA-drug resistance associations.
To improve the reliability of ncRNA-drug resistance association prediction by addressing noise in biological data.

Main Methods:

Constructed an original ncRNA-drug resistance association bipartite graph.
Employed a bi-view skeleton extraction strategy and a graph neural network estimator to optimize graph structures and learn embeddings.
Utilized adaptive attentional feature fusion for final edge embedding and prediction of potential associations.

Main Results:

RDRGSE demonstrated significant advantages in discovering ncRNA-drug resistance associations.
Achieved a 6.7% improvement in AUC and a 6.1% improvement in AUPR compared to state-of-the-art methods.
Ablation studies and case studies confirmed the framework's generalization ability and robustness.

Conclusions:

RDRGSE offers a powerful computational method for predicting ncRNA-drug resistance associations.
The framework can serve as an effective screening tool for identifying drug resistance biomarkers.