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AFExNet: An Adversarial Autoencoder for Differentiating Breast Cancer Sub-types and Extracting Biologically Relevant Genes.
IEEE/ACM Transactions on Computational Biology and Bioinformatics ( IF 3.6 ) Pub Date : 2021-03-15 , DOI: 10.1109/tcbb.2021.3066086
Raktim Kumar Mondol , Nhan Duy Truong , Mohammad Reza , Samuel Ippolito , Esmaeil Ebrahimie , Omid Kavehei

Technological advancements in high-throughput genomics enable the generation of complex and large data sets that can be used for classification, clustering, and bio-marker identification. Modern deep learning algorithms provide us with the opportunity of finding most significant features in such huge dataset to characterize diseases (e.g., cancer) and their sub-types. Thus, developing such deep learning method, which can successfully extract meaningful features from various breast cancer sub-types, is of current research interest. In this paper, we develop dual stage (unsupervised pre-training and supervised fine-tuning) neural network architecture termed AFExNet based on adversarial auto-encoder (AAE) to extract features from high dimensional genetic data. We evaluated the performance of our model through twelve different supervised classifiers to verify the usefulness of the new features using public RNA-Seq dataset of breast cancer. AFExNet provides consistent results in all performance metrics across twelve different classifiers which makes our model classifier independent. We also develop a method named "TopGene" to find highly weighted genes from the latent space which could be useful for finding cancer bio-markers. Put together, AFExNet has great potential for biological data to accurately and effectively extract features. Our work is fully reproducible and source code can be downloaded from Github: https://github.com/NeuroSyd/breast-cancer-sub-types.

中文翻译:

AFExNet:一种对抗性自动编码器,用于区分乳腺癌亚型和提取生物学相关基因。

高通量基因组学的技术进步使得能够生成复杂的大型数据集,这些数据集可用于分类,聚类和生物标记识别。现代深度学习算法为我们提供了在如此庞大的数据集中找到最重要特征以表征疾病(例如癌症)及其亚型的机会。因此,开发一种可以从各种乳腺癌亚型中成功提取有意义特征的深度学习方法,具有当前的研究兴趣。在本文中,我们基于对抗性自动编码器(AAE)开发了称为AFExNet的双阶段(无监督预训练和有监督微调)神经网络体系结构,以从高维遗传数据中提取特征。我们通过十二个不同的监督分类器评估了模型的性能,以使用乳腺癌的公共RNA-Seq数据集验证新功能的有效性。AFExNet在十二个不同的分类器上的所有性能指标中提供一致的结果,这使我们的模型分类器独立。我们还开发了一种名为“ TopGene”的方法,可以从潜在空间中找到高度加权的基因,这可能对发现癌症生物标记有用。综上所述,AFExNet在生物数据准确有效地提取特征方面具有巨大的潜力。我们的工作是完全可复制的,可以从Github下载源代码:https://github.com/NeuroSyd/breast-cancer-sub-types。AFExNet在十二个不同的分类器上的所有性能指标中提供一致的结果,这使我们的模型分类器独立。我们还开发了一种名为“ TopGene”的方法,可以从潜在空间中找到高度加权的基因,这可能对发现癌症生物标记有用。综上所述,AFExNet在生物数据准确有效地提取特征方面具有巨大的潜力。我们的工作是完全可复制的,可以从Github下载源代码:https://github.com/NeuroSyd/breast-cancer-sub-types。AFExNet在十二个不同的分类器上的所有性能指标中提供一致的结果,这使我们的模型分类器独立。我们还开发了一种名为“ TopGene”的方法,可以从潜在空间中找到高度加权的基因,这可能对发现癌症生物标记有用。综上所述,AFExNet在生物数据准确有效地提取特征方面具有巨大的潜力。我们的工作是完全可复制的,可以从Github下载源代码:https://github.com/NeuroSyd/breast-cancer-sub-types。AFExNet在生物数据准确有效地提取特征方面具有巨大潜力。我们的工作是完全可复制的,可以从Github下载源代码:https://github.com/NeuroSyd/breast-cancer-sub-types。AFExNet在生物数据准确有效地提取特征方面具有巨大潜力。我们的工作是完全可复制的,可以从Github下载源代码:https://github.com/NeuroSyd/breast-cancer-sub-types。
更新日期:2021-03-15
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