Explainable Deep Learning Framework for SERS Bio-quantification

TL;DR

This paper presents a novel explainable deep learning framework for SERS bio-quantification, combining spectral processing, biomarker quantification, and interpretability to enhance biomarker discovery and analysis.

Contribution

It introduces a new SERS bio-quantification framework with spectral enhancement, CNN and transformer models, and a novel CRIME interpretability method for SERS analysis.

Findings

Effective serotonin quantification with low error using CNN on denoised spectra

CRIME method identified six prediction contexts, three related to serotonin

Framework enables rapid, inexpensive biomarker discovery from SERS data

Abstract

Surface-enhanced Raman spectroscopy (SERS) is a potential fast and inexpensive method of analyte quantification, which can be combined with deep learning to discover biomarker-disease relationships. This study aims to address present challenges of SERS through a novel SERS bio-quantification framework, including spectral processing, analyte quantification, and model explainability. To this end,serotonin quantification in urine media was assessed as a model task with 682 SERS spectra measured in a micromolar range using cucurbit[8]uril chemical spacers. A denoising autoencoder was utilized for spectral enhancement, and convolutional neural networks (CNN) and vision transformers were utilized for biomarker quantification. Lastly, a novel context representative interpretable model explanations (CRIME) method was developed to suit the current needs of SERS mixture analysis explainability. Serotonin quantification was most efficient in denoised spectra analysed using a convolutional neural network with a three-parameter logistic output layer (mean absolute error = 0.15 {\mu}M, mean percentage error = 4.67%). Subsequently, the CRIME method revealed the CNN model to present six prediction contexts, of which three were associated with serotonin. The proposed framework could unlock a novel, untargeted hypothesis generating method of biomarker discovery considering the rapid and inexpensive nature of SERS measurements, and the potential to identify biomarkers from CRIME contexts.