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：

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AFExNet：用于区分乳腺癌亚型和提取生物学相关基因的对抗性自动编码器


高通量基因组学的技术进步能够生成复杂的大型数据集，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的机会，以表征疾病（例如癌症）及其子类型。因此，开发这种能够成功地从各种乳腺癌亚型中提取有意义的特征的深度学习方法是当前的研究兴趣。在本文中，我们开发了基于对抗性自动编码器（AAE）的双阶段（无监督预训练和监督微调）神经网络架构，称为 AFExNet，以从高维遗传数据中提取特征。我们通过 12 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 数据集验证新特征的有用性。 AFExNet 在十二个不同分类器的所有性能指标中提供一致的结果，这使得我们的模型分类器独立。我们还开发了一种名为“TopGene”的方法，从潜在空间中寻找高权重基因，这可能有助于寻找癌症生物标志物。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潜力。我们的工作是完全可重复的，源代码可以从 Github 下载：https://github.com/NeuroSyd/breast-cancer-sub-types。