Related papers: A Graph Based Raman Spectral Processing Technique …
Raman scattering and photoluminescence spectroscopy are used to investigate the optical properties of single layer black phosphorus obtained by mechanical exfoliation of bulk crystals under an argon atmosphere. The Raman spectroscopy,…
Miniaturized spectrometers for Raman spectroscopy have the potential to open up a new chapter in sensing. Raman spectroscopy is essential for material characterization and biomedical diagnostics, however, its weak signal and the need for…
Infrared spectroscopy is the technique of choice for chemical identification of biomolecules through their vibrational fingerprints. However, infrared light interacts poorly with nanometric size molecules. Here, we exploit the unique…
Raman spectroscopy is widely used across scientific domains to characterize the chemical composition of samples in a non-destructive, label-free manner. Many applications entail the unmixing of signals from mixtures of molecular species to…
Undoubtedly, Raman spectroscopy is one of the most elaborated spectroscopy tools in materials science, chemistry, medicine and optics. However, when it comes to the analysis of nanostructured specimens, accessing the Raman spectra resulting…
Raman spectroscopy's capability to provide meaningful composition predictions is heavily reliant on a pre-processing step to remove insignificant spectral variation. This is crucial in biofluid analysis. Widespread adoption of diagnostics…
Surface Enhanced Raman Spectroscopy (SERS) is a well-established technique for enhancing Raman signals. Recently photonic integrated circuits have been used, as an alternative to microscopy based excitation and collection, to probe SERS…
Raman spectroscopy is widely applied to detect different chemical compounds in organic matter and create label-free high-resolution maps on the level of separate cells. The main advantage of this technique is the possibility to study…
Detection of biomolecules is important in proteomics and clinical diagnosis and treatment of diseases. Here, we apply functionalized, macromolecular, single walled carbon nanotubes SWNTs as multi-color Raman labels to protein arrays for…
Omics data, such as transcriptomics, proteomics, and metabolomics, provide critical insights into disease mechanisms and clinical outcomes. However, their high dimensionality, small sample sizes, and intricate biological networks pose major…
Graph signal processing (GSP) is becoming a major tool in biomedical signal and image analysis. In most GSP techniques, graph structures and edge weights have been typically set via statistical and computational methods. More recently,…
Electrochemical exfoliation is one of the most promising methods for scalable production of graphene. However, limited understanding of its Raman spectrum as well as lack of measurement standards for graphene strongly limit its industrial…
Biomarker identification is critical for precise disease diagnosis and understanding disease pathogenesis in omics data analysis, like using fold change and regression analysis. Graph neural networks (GNNs) have been the dominant deep…
Spectral graph convolutional neural networks (GCNNs) have been producing encouraging results in graph classification tasks. However, most spectral GCNNs utilize fixed graphs when aggregating node features, while omitting edge feature…
The graph isomorphism problem is a main problem which has numerous applications in different fields. Thus, finding an efficient and easy to implement method to discriminate non-isomorphic graphs is valuable. In this paper, a new method is…
Graph Neural Networks (GNNs) have emerged as a powerful approach for graph-based machine learning tasks. Previous work applied GNNs to image-derived graph representations for various downstream tasks such as classification or anomaly…
Surface enhanced Raman scattering (SERS) exploits surface plasmons induced by the incident field in metallic nanostructures to significantly increase the Raman intensity. Graphene provides the ideal prototype two dimensional (2d) test…
Machine learning techniques have recently been adopted in various applications in medicine, biology, chemistry, and material engineering. An important task is to predict the properties of molecules, which serves as the main subroutine in…
Surface-enhanced Raman spectroscopy is a powerful and versatile sensing method with a detection limit down to the single molecule level. In this article, we demonstrate how topology optimization (TopOpt) can be used for designing surface…
Spectral Graph Neural Networks (SGNNs) have achieved remarkable performance in tasks such as node classification due to their ability to learn flexible filters. Typically, these filters are learned under the supervision of downstream tasks,…