Occlusion enhanced pan-cancer classification via deep learning.

IF 2.9 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS BMC Bioinformatics Pub Date : 2024-08-08 DOI:10.1186/s12859-024-05870-y
Xing Zhao, Zigui Chen, Huating Wang, Hao Sun
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Abstract

Quantitative measurement of RNA expression levels through RNA-Seq is an ideal replacement for conventional cancer diagnosis via microscope examination. Currently, cancer-related RNA-Seq studies focus on two aspects: classifying the status and tissue of origin of a sample and discovering marker genes. Existing studies typically identify marker genes by statistically comparing healthy and cancer samples. However, this approach overlooks marker genes with low expression level differences and may be influenced by experimental results. This paper introduces "GENESO," a novel framework for pan-cancer classification and marker gene discovery using the occlusion method in conjunction with deep learning. we first trained a baseline deep LSTM neural network capable of distinguishing the origins and statuses of samples utilizing RNA-Seq data. Then, we propose a novel marker gene discovery method called "Symmetrical Occlusion (SO)". It collaborates with the baseline LSTM network, mimicking the "gain of function" and "loss of function" of genes to evaluate their importance in pan-cancer classification quantitatively. By identifying the genes of utmost importance, we then isolate them to train new neural networks, resulting in higher-performance LSTM models that utilize only a reduced set of highly relevant genes. The baseline neural network achieves an impressive validation accuracy of 96.59% in pan-cancer classification. With the help of SO, the accuracy of the second network reaches 98.30%, while using 67% fewer genes. Notably, our method excels in identifying marker genes that are not differentially expressed. Moreover, we assessed the feasibility of our method using single-cell RNA-Seq data, employing known marker genes as a validation test.

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通过深度学习增强闭塞泛癌症分类。
通过 RNA-Seq 对 RNA 表达水平进行定量测量,是取代传统显微镜检查进行癌症诊断的理想方法。目前,与癌症相关的 RNA-Seq 研究主要集中在两个方面:对样本的状态和来源组织进行分类以及发现标记基因。现有研究通常通过对健康样本和癌症样本进行统计比较来确定标记基因。然而,这种方法会忽略表达水平差异较小的标记基因,而且可能会受到实验结果的影响。我们首先训练了一个基线深度 LSTM 神经网络,该网络能够利用 RNA-Seq 数据区分样本的来源和状态。然后,我们提出了一种名为 "对称闭塞(SO)"的新型标记基因发现方法。它与基线 LSTM 网络合作,模仿基因的 "功能增益 "和 "功能丧失",定量评估基因在泛癌症分类中的重要性。通过识别最重要的基因,我们将它们分离出来训练新的神经网络,从而得到只使用较少高度相关基因集的更高性能 LSTM 模型。基线神经网络在泛癌症分类中达到了令人印象深刻的 96.59% 验证准确率。在 SO 的帮助下,第二个网络的准确率达到了 98.30%,而使用的基因却减少了 67%。值得注意的是,我们的方法在识别无差异表达的标记基因方面表现出色。此外,我们还利用单细胞 RNA-Seq 数据评估了我们方法的可行性,并采用已知的标记基因作为验证测试。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
BMC Bioinformatics
BMC Bioinformatics 生物-生化研究方法
CiteScore
5.70
自引率
3.30%
发文量
506
审稿时长
4.3 months
期刊介绍: BMC Bioinformatics is an open access, peer-reviewed journal that considers articles on all aspects of the development, testing and novel application of computational and statistical methods for the modeling and analysis of all kinds of biological data, as well as other areas of computational biology. BMC Bioinformatics is part of the BMC series which publishes subject-specific journals focused on the needs of individual research communities across all areas of biology and medicine. We offer an efficient, fair and friendly peer review service, and are committed to publishing all sound science, provided that there is some advance in knowledge presented by the work.
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