Clinically Applicable Deep Learning Algorithm Using Quantitative Proteomic Data

Abstract

Deep learning (DL), a type of machine learning approach, is a powerful tool for analyzing large sets of data that are derived from biomedical sciences. However, it remains unknown whether DL is suitable for identifying contributing factors, such as biomarkers, in quantitative proteomics data. In this study, we describe an optimized DL-based analytical approach using a data set that was generated by selected reaction monitoring-mass spectrometry (SRM-MS), comprising SRM-MS data from 1008 samples for the diagnosis of pancreatic cancer, to test its classification power. Its performance was compared with that of 5 conventional multivariate and machine learning methods: random forest (RF), support vector machine (SVM), logistic regression (LR), k-nearest neighbors (k-NN), and naïve Bayes (NB). The DL method yielded the best classification (AUC 0.9472 for the test data set) of all approaches. We also optimized the parameters of DL individually to determine which factors were the most significant. In summary, the DL method has advantages in classifying the quantitative proteomics data of pancreatic cancer patients, and our results suggest that its implementation can improve the performance of diagnostic assays in clinical settings.

Keywords: SRM−MS; deep learning; machine learning; mass spectrometry; targeted proteomics.