Related papers: A Graph Based Raman Spectral Processing Technique …
Computer vision models are increasingly capable of classifying ovarian epithelial cancer subtypes, but they differ from pathologists by processing small tissue patches at a single resolution. Multi-resolution graph models leverage the…
We present a hybrid surface-enhanced Raman spectroscopy (SERS) platform based on dielectric silicon metasurfaces integrated with functionalized graphene for the selective detection of biomolecules such as prolactin and SARS-CoV-2…
The impact of the environment on graphene's properties such as strain, charge density, and dielectric environment can be evaluated by Raman spectroscopy. These environmental interactions are not trivial to determine, since they affect the…
Graph embedding techniques have been increasingly deployed in a multitude of different applications that involve learning on non-Euclidean data. However, existing graph embedding models either fail to incorporate node attribute information…
Mass spectrometry is the dominant technology in the field of proteomics, enabling high-throughput analysis of the protein content of complex biological samples. Due to the complexity of the instrumentation and resulting data, sophisticated…
Graph Neural Networks (GNNs) have shown remarkable merit in performing various learning-based tasks in complex networks. The superior performance of GNNs often correlates with the availability and quality of node-level features in the input…
This work presents an innovative magnetoelastic (ME) biosensor using graphene functionalized with the SARS-CoV-2 N protein for antibody detection via magnetoelastic resonance. Graphene was chosen for its biocompatibility and high surface…
This paper proposes a novel Extended Particle Swarm Optimization model (EPSO) that potentially enhances the search process of PSO for optimization problem. Evidently, gene expression profiles are significantly important measurement factor…
An efficient route to synthesize macroscopic amounts of graphene is highly desired and a bulk characterization of such samples, in terms of the number of layers, is equally important. We present a Raman spectroscopy-based method to…
Spectroscopic analysis of large biomolecules is critical in a number of applications, including medical diagnostics and label-free biosensing. Recently, it has been shown that Raman spectroscopy of proteins can be used to diagnose some…
Spectral Graph Neural Networks (GNNs), alternatively known as graph filters, have gained increasing prevalence for heterophily graphs. Optimal graph filters rely on Laplacian eigendecomposition for Fourier transform. In an attempt to avert…
An early detection of different tumor subtypes is crucial for an effective guidance to personalized therapy. While much efforts focus on decoding the sequence of DNA basis to detect the genetic mutations related to cancer, it is becoming…
A simple, non-invasive method using Raman spectroscopy for the estimation of the thickness of graphene layers grown epitaxially on silicon carbide (SiC) is presented, enabling simultaneous determination of thickness, grain size and disorder…
Ultra-fine-grained image recognition (UFGIR) categorizes objects with extremely small differences between classes, such as distinguishing between cultivars within the same species, as opposed to species-level classification in fine-grained…
Raman spectroscopy is a powerful tool for material characterization. However, careful preprocessing is required for the identification and handling of noise, baseline drift, and random spikes. This paper presents a comprehensive approach to…
The unique properties of graphene offer immense opportunities for applications to many scientific fields, as well as societal needs, beyond our present imagination. One of the important features of graphene is the relatively simple…
DNA/RNA synthesis precursors are especially vulnerable to damage induced by reactive oxygen species occurring following oxidative stress. Guanosine triphosphates are the prevalent oxidized nucleosites, which can be misincorporated during…
Graph Neural Networks (GNNs) extend the success of neural networks to graph-structured data by accounting for their intrinsic geometry. While extensive research has been done on developing GNN models with superior performance according to a…
Avoiding charge density fluctuations and impurities in graphene is vital for high-quality graphene-based devices. Traditional characterization methods require device fabrication and electrical transport measurements, which are…
In this study, the combination of a developing data mining technique called statistical decision tree analysis method and Raman spectroscopy was proposed to differentiate human normal leukocytes from malignant tumor cells. Statistical…