ポスター：敵対的生成ネットワークを用いたLC-MSデータのAIノイズ除去 - PoC

10月 15, 2024

本ポスターでは、LC-MSデータのノイズ除去において、条件付き敵対的生成ネットワーク（cGAN）を用いたAIによる新たな前処理方法を紹介しています。多様なデータセットにわたって、専門家によるノイズ除去パラメータのチューニングを不要にします。

Denoising parameterization traditionally requires expert understanding and monitoring, and presents a source of variability. Here, we present a novel, AI-based, pre-processing method for denoising Liquid Chromatography-Mass Spectrometry (LC-MS) data using conditional Generative Adversarial Networks (cGANs). This eliminates the need for any parameterization of the denoising method.
The new method achieves comparable performance vs. state-of-the-art methods, especially by scoring on downstream results, e.g., regarding coverage of Heavy Chain (HC) and Light Chain (LC) or the critical quality attributes (CQAs) in LC-MS-based MAM data analysis.
This workflow demonstrates robustness across diverse data sets and doesn’t require parameterization or other tuning. It helps to further automate data analysis, and enhancing efficiency and reliability. This innovative approach highlights the potential of AI assisted analysis for complex LC-MS data.