Clinically Applicable Deep Learning Algorithm Using Quantitative Proteomic Data

Cited 15 time in Web of Science Cited 15 time in Scopus

Kim, Hyunsoo; Kim, Yoseop; Han, Buhm; Jang, Jin-Young; Kim, Youngsoo

Issue Date
American Chemical Society
Journal of Proteome Research, Vol.18 No.8, pp.3195-3202
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 naive 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.
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College of Medicine/School of Medicine (의과대학/대학원)Dept. of Biomedical Sciences (대학원 의과학과)Journal Papers (저널논문_의과학과)
College of Medicine/School of Medicine (의과대학/대학원)Dept. of Medicine (의학과)Journal Papers (저널논문_의학과)
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