Biostatistics Department, Princess Margaret Cancer Center, University Health Network, Toronto, ON, M5G2C4, Canada.
Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, M5T3M7, Canada.
Bioinformatics. 2024 Mar 29;40(4). doi: 10.1093/bioinformatics/btae161.
Research is improving our understanding of how the microbiome interacts with the human body and its impact on human health. Existing machine learning methods have shown great potential in discriminating healthy from diseased microbiome states. However, Machine Learning based prediction using microbiome data has challenges such as, small sample size, imbalance between cases and controls and high cost of collecting large number of samples. To address these challenges, we propose a deep learning framework phylaGAN to augment the existing datasets with generated microbiome data using a combination of conditional generative adversarial network (C-GAN) and autoencoder. Conditional generative adversarial networks train two models against each other to compute larger simulated datasets that are representative of the original dataset. Autoencoder maps the original and the generated samples onto a common subspace to make the prediction more accurate.
Extensive evaluation and predictive analysis was conducted on two datasets, T2D study and Cirrhosis study showing an improvement in mean AUC using data augmentation by 11% and 5% respectively. External validation on a cohort classifying between obese and lean subjects, with a smaller sample size provided an improvement in mean AUC close to 32% when augmented through phylaGAN as compared to using the original cohort. Our findings not only indicate that the generative adversarial networks can create samples that mimic the original data across various diversity metrics, but also highlight the potential of enhancing disease prediction through machine learning models trained on synthetic data.
研究正在提高我们对微生物组与人体相互作用及其对人类健康影响的理解。现有的机器学习方法在区分健康和患病微生物组状态方面显示出了巨大的潜力。然而，基于机器学习的微生物组数据分析在小样本量、病例与对照组之间的不平衡以及收集大量样本的高成本等方面存在挑战。为了解决这些挑战，我们提出了一个深度学习框架 phylaGAN，使用条件生成对抗网络（C-GAN）和自动编码器组合，用生成的微生物组数据来扩充现有的数据集。条件生成对抗网络训练两个模型相互竞争，以计算更具代表性的原始数据集的更大模拟数据集。自动编码器将原始样本和生成样本映射到一个公共子空间，以提高预测的准确性。
在两个数据集（T2D 研究和肝硬化研究）上进行了广泛的评估和预测分析，数据扩充后平均 AUC 分别提高了 11%和 5%。在一个将肥胖和消瘦受试者分类的队列上进行外部验证，当使用 phylaGAN 进行扩充时，与使用原始队列相比，平均 AUC 提高了近 32%，样本量较小。我们的研究结果不仅表明生成对抗网络可以生成在各种多样性指标上模仿原始数据的样本，还强调了通过在合成数据上训练的机器学习模型增强疾病预测的潜力。
