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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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相关实验视频

Updated: May 5, 2026

Identification of Circular RNAs using RNA Sequencing

Identification of Circular RNAs using RNA Sequencing

Published on: November 14, 2019

循环RNA-药物关联预测基于多尺度卷积神经网络和对抗性自编码器.

Yao Wang¹, Xiujuan Lei¹, Yuli Chen¹

¹School of Computer Science, Shaanxi Normal University, Xi'an 710119, China.

International journal of molecular sciences

|February 26, 2025

概括

预测循环RNA (circRNA) 药物关联是新疗法的关键. 我们的AAECDA模型使用多尺度CNN和对抗性自动编码器来准确识别这些关键的治疗点.

关键词:

对抗性的自动编码器.循环的RNA-药物协会预测预测.多尺度卷积神经网络多尺度卷积神经网络

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科学领域:

生物信息学是一种生物信息学.
计算生物学计算生物学
基因组学就是基因组学.

背景情况:

循环RNAs (circRNAs) 在疾病机制和药物发现中至关重要.
由于复杂的生物数据和网络,预测circRNA药物关联是具有挑战性的.
现有的方法面临异质网络和高维生物数据的局限性.

研究的目的:

开发一种先进的计算方法来预测circRNA与药物之间的关联.
利用多尺度卷积神经网络 (MSCNN) 和对抗性自动编码器来提高预测准确性.
通过准确地绘制circRNA药物相互作用,识别新的治疗点.

主要方法:

构建了一个整合circRNA序列相似性,药物结构相似性和已知的circRNA-药物关联的特征网络.
使用MSCNN从集成网络中进行层次特征提取.
利用对抗性的自动编码器来改进特征并获得用于预测的低维表示.

主要成果:

拟议的AAECDA模型与现有的基线方法相比,显示出更高的性能.
实验结果验证了该模型在预测circRNA药物关联方面的有效性.
案例研究证实了AAECDA在相关生物任务中的实际应用.

结论:

AAECDA提供了一种强大而有效的方法来预测circRNA与药物之间的关联.
该模型有助于在疾病研究中识别潜在的治疗点.
这种方法为药物发现和个性化医学提供了宝贵的工具.