最近开发了基于深度学习的框架，用于从高通量基因表达谱中识别癌症亚型。不幸的是，深度学习的性能高度依赖于其神经网络架构，而神经网络架构通常是由深度神经网络（DNN）专业知识手工制作的，同时，网络的优化和调整通常成本高昂且耗时。为了克服局限性，我们提出了一种全自动深度神经架构搜索模型，用于从基因表达数据（DNAS）中诊断一致的分子亚型。该模型使用启发式群体智能算法之一的蚁群算法来搜索和优化神经网络架构，它可以在其搜索空间中自动找到用于癌症诊断的最佳深度学习模型架构。我们在八个结直肠癌数据集上验证了 DNAS，平均准确度为 95.48%，平均特异性为 98.07%，平均灵敏度为 96.24%。不失一般性，我们进一步研究了DNAS在不同平台的其他癌症类型（包括肺癌和乳腺癌）上的普遍适用性，DNAS的AUC分别达到了95%和96%。此外，我们还进行了基因本体丰富和病理分析，以揭示多种癌症类型的癌症亚型识别和表征的有趣见解。源代码和数据可以从https://github.com/userd113/DNAS-main下载。 DNAS 的网络服务器可通过 119.45.145.120:5001 公开访问。补充数据可在 Bioinformatics online 上获取。© 作者 2023。由牛津大学出版社出版。

Recent frameworks based on deep learning have been developed to identify cancer subtypes from high-throughput gene expression profiles. Unfortunately, the performance of deep learning is highly dependent on its neural network architectures which are often hand-crafted with expertise in deep neural networks (DNNs), meanwhile, the optimization and adjustment of the network are usually costly and time consuming.To address such limitations, we proposed a fully-automated deep neural architecture search model for diagnosing consensus molecular subtypes from gene expression data (DNAS). The proposed model uses ant colony algorithm, one of the heuristic swarm intelligence algorithms, to search and optimize neural network architecture, and it can automatically find the optimal deep learning model architecture for cancer diagnosis in its search space. We validated DNAS on eight colorectal cancer datasets, achieving the average accuracy of 95.48%, the average specificity of 98.07%, and the average sensitivity of 96.24% respectively. Without the loss of generality, we investigated the general applicability of DNAS further on other cancer types from different platforms including lung cancer and breast cancer, and DNAS achieved an AUC of 95% and 96% respectively. In addition, we conducted gene ontology enrichment and pathological analysis to reveal interesting insights into cancer subtype identification and characterization across multiple cancer types.The source code and data can be downloaded from https://github.com/userd113/DNAS-main. And the web server of DNAS is publicly accessible at 119.45.145.120:5001.Supplementary data are available at Bioinformatics online.© The Author(s) 2023. Published by Oxford University Press.