Explainable Deep Learning Framework for SERS Bio-quantification

• URL: http://arxiv.org/abs/2411.08082v1
• Date: Tue, 12 Nov 2024 11:26:56 GMT
• Title: Explainable Deep Learning Framework for SERS Bio-quantification
• Authors: Jihan K. Zaki, Jakub Tomasik, Jade A. McCune, Sabine Bahn, Pietro Liò, Oren A. Scherman,
• Abstract summary: This study aims to address present challenges of surface-enhanced Raman spectroscopy (SERS) through a novel SERS bio-quantification framework. 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 novel context representative interpretable model explanations (CRIME) method was developed to suit the current needs of SERS mixture analysis explainability.
• Score: 12.855316833585908
• License:
• 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.

Related papers

• Dumpling GNN: Hybrid GNN Enables Better ADC Payload Activity Prediction Based on Chemical Structure [53.76752789814785]
  DumplingGNN is a hybrid Graph Neural Network architecture specifically designed for predicting ADC payload activity based on chemical structure. We evaluate it on a comprehensive ADC payload dataset focusing on DNA Topoisomerase I inhibitors. It demonstrates exceptional accuracy (91.48%), sensitivity (95.08%), and specificity (97.54%) on our specialized ADC payload dataset.
  arXiv  Detail & Related papers  (2024-09-23T17:11:04Z)
• Variational and Explanatory Neural Networks for Encoding Cancer Profiles and Predicting Drug Responses [40.80133767939435]
  Existing AI models face challenges due to noise in transcriptomics data and lack of biological interpretability. We introduce VETE, a novel neural network framework that incorporates a variational component to mitigate noise effects. VETE bridges the gap between AI-driven predictions and biologically meaningful insights in cancer research.
  arXiv  Detail & Related papers  (2024-07-05T13:13:02Z)
• Discovering robust biomarkers of neurological disorders from functional MRI using graph neural networks: A Review [4.799269666410891]
  We provide an overview of how GNN and model explainability techniques have been applied on fMRI datasets for disorder prediction tasks. We find that while most studies have performant models, salient features highlighted in these studies vary greatly across studies on the same disorder. We suggest establishing new standards that are based on objective evaluation metrics to determine the robustness of these potential biomarkers.
  arXiv  Detail & Related papers  (2024-05-01T15:29:55Z)
• Deep Learning Predicts Biomarker Status and Discovers Related Histomorphology Characteristics for Low-Grade Glioma [21.281553456323998]
  Biomarker detection is an indispensable part in the diagnosis and treatment of low-grade glioma (LGG) We propose an interpretable deep learning pipeline to predict the status of five biomarkers in LGG using only hematoxylin and eosin-stained whole slide images and slide-level biomarker status labels. Our pipeline not only provides a novel approach for biomarker prediction, enhancing the applicability of molecular treatments for LGG patients but also facilitates the discovery of new mechanisms in molecular functionality and LGG progression.
  arXiv  Detail & Related papers  (2023-10-11T13:05:33Z)
• Breast Ultrasound Tumor Classification Using a Hybrid Multitask CNN-Transformer Network [63.845552349914186]
  Capturing global contextual information plays a critical role in breast ultrasound (BUS) image classification. Vision Transformers have an improved capability of capturing global contextual information but may distort the local image patterns due to the tokenization operations. In this study, we proposed a hybrid multitask deep neural network called Hybrid-MT-ESTAN, designed to perform BUS tumor classification and segmentation.
  arXiv  Detail & Related papers  (2023-08-04T01:19:32Z)
• A marker-less human motion analysis system for motion-based biomarker discovery in knee disorders [60.99112047564336]
  The NHS has been having increased difficulty seeing all low-risk patients, this includes but not limited to suspected osteoarthritis (OA) patients. We propose a novel method of automated biomarker identification for diagnosis of knee disorders and the monitoring of treatment progression.
  arXiv  Detail & Related papers  (2023-04-26T16:47:42Z)
• Intelligent diagnostic scheme for lung cancer screening with Raman spectra data by tensor network machine learning [10.813777115744362]
  We propose a tensor-network (TN)-ML method to reliably predict lung cancer patients and their stages via screening Raman spectra data of Volatile organic compounds (VOCs) in exhaled breath. The accuracy of the samples with high certainty is almost 100$%$.
  arXiv  Detail & Related papers  (2023-03-11T07:57:37Z)
• Evaluation of the potential of Near Infrared Hyperspectral Imaging for monitoring the invasive brown marmorated stink bug [53.682955739083056]
  The brown marmorated stink bug (BMSB), Halyomorpha halys, is an invasive insect pest of global importance that damages several crops. The present study consists in a preliminary evaluation at the laboratory level of Near Infrared Hyperspectral Imaging (NIR-HSI) as a possible technology to detect BMSB specimens.
  arXiv  Detail & Related papers  (2023-01-19T11:37:20Z)
• Atlas-powered deep learning (ADL) -- application to diffusion weighted MRI [8.219843232619551]
  We propose the first framework to exploit both deep learning and atlases for biomarker estimation in dMRI. Our framework relies on non-linear diffusion registration tensor to compute biomarker atlases and to estimate atlas reliability maps. We use our framework to estimate an fractionalisotropy and neurite orientation dispersion from down-sampled dMRI data on a test cohort of 70 newborn subjects.
  arXiv  Detail & Related papers  (2022-05-05T17:21:47Z)
• Mixed Precision Low-bit Quantization of Neural Network Language Models for Speech Recognition [67.95996816744251]
  State-of-the-art language models (LMs) represented by long-short term memory recurrent neural networks (LSTM-RNNs) and Transformers are becoming increasingly complex and expensive for practical applications. Current quantization methods are based on uniform precision and fail to account for the varying performance sensitivity at different parts of LMs to quantization errors. Novel mixed precision neural network LM quantization methods are proposed in this paper.
  arXiv  Detail & Related papers  (2021-11-29T12:24:02Z)
• G-MIND: An End-to-End Multimodal Imaging-Genetics Framework for Biomarker Identification and Disease Classification [49.53651166356737]
  We propose a novel deep neural network architecture to integrate imaging and genetics data, as guided by diagnosis, that provides interpretable biomarkers. We have evaluated our model on a population study of schizophrenia that includes two functional MRI (fMRI) paradigms and Single Nucleotide Polymorphism (SNP) data.
  arXiv  Detail & Related papers  (2021-01-27T19:28:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.