Deep Learning Framework for Integrating Multibatch Calibration, Classification, and Pathway Activities,Analytical Chemistry

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Deep Learning Framework for Integrating Multibatch Calibration, Classification, and Pathway Activities
Analytical Chemistry ( IF 6.7 ) Pub Date : 2022-06-16 , DOI: 10.1021/acs.analchem.2c00601
JingYang Niu ₁ , Wei Xu ₁ , DongMing Wei ₁ , Kun Qian ₁ , Qian Wang ₂

Affiliation

The amount of available biological data has exploded since the emergence of high-throughput technologies, which is not only revolting the way we recognize molecules and diseases but also bringing novel analytical challenges to bioinformatics analysis. In recent years, deep learning has become a dominant technique in data science. However, classification accuracy is plagued with domain discrepancy. Notably, in the presence of multiple batches, domain discrepancy typically happens between individual batches. Most pairwise adaptation approaches may be suboptimal as they fail to eliminate external factors across multiple batches and take the classification task into account simultaneously. We propose a joint deep learning framework for integrating batch effect removal, classification, and downstream pathway activities upon biological data. To this end, we validate it on two MALDI MS-based metabolomics datasets. We have achieved the highest diagnostic accuracy (ACC), with a notable ∼10% improvement over other methods. Overall, these results indicate that our approach removes batch effect more effectively than state-of-the-art methods and yields more accurate classification as well as biomarkers for smart diagnosis.

中文翻译：

用于集成多批次校准、分类和通路活动的深度学习框架

自高通量技术出现以来，可用生物数据的数量呈爆炸式增长，这不仅颠覆了我们识别分子和疾病的方式，也给生物信息学分析带来了新的分析挑战。近年来，深度学习已成为数据科学中的主导技术。然而，分类准确性受到领域差异的困扰。值得注意的是，在存在多个批次的情况下，域差异通常发生在各个批次之间。大多数成对适应方法可能不是最理想的，因为它们无法消除跨多个批次的外部因素并同时考虑分类任务。我们提出了一个联合深度学习框架，用于在生物数据上集成批量效应去除、分类和下游通路活动。为此，我们在两个基于 MALDI MS 的代谢组学数据集上对其进行了验证。我们已经实现了最高的诊断准确度 (ACC)，与其他方法相比显着提高了 10%。总体而言，这些结果表明，我们的方法比最先进的方法更有效地消除了批次效应，并产生了更准确的分类以及用于智能诊断的生物标志物。

更新日期：2022-06-16

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