Zobrazeno 1 - 10 of 1 808 pro vyhledávání: '"Stevens, Molly"'
1
Report
Hyperspectral unmixing for Raman spectroscopy via physics-constrained autoencoders
Autor: Georgiev, Dimitar, Fernández-Galiana, Álvaro, Pedersen, Simon Vilms, Papadopoulos, Georgios, Xie, Ruoxiao, Stevens, Molly M., Barahona, Mauricio
Publikováno v: Proceedings of the National Academy of Sciences, 2024, 121(44), e2321305121
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 identify t
Externí odkaz: http://arxiv.org/abs/2403.04526
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2
Report
Transfer Learning Bayesian Optimization to Design Competitor DNA Molecules for Use in Diagnostic Assays
Autor: Sedgwick, Ruby, Goertz, John P., Stevens, Molly M., Misener, Ruth, van der Wilk, Mark
With the rise in engineered biomolecular devices, there is an increased need for tailor-made biological sequences. Often, many similar biological sequences need to be made for a specific application meaning numerous, sometimes prohibitively expensive
Externí odkaz: http://arxiv.org/abs/2402.17704
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4
Report
RamanSPy: An open-source Python package for integrative Raman spectroscopy data analysis
Autor: Georgiev, Dimitar, Pedersen, Simon Vilms, Xie, Ruoxiao, Fernández-Galiana, Álvaro, Stevens, Molly M., Barahona, Mauricio
Publikováno v: Anal. Chem. 2024, 96, 21, 8492-8500
Raman spectroscopy is a non-destructive and label-free chemical analysis technique, which plays a key role in the analysis and discovery cycle of various branches of science. Nonetheless, progress in Raman spectroscopic analysis is still impeded by t
Externí odkaz: http://arxiv.org/abs/2307.13650
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6
Report
Unsupervised hyperspectral data mining and bioimaging by information entropy and self-modeling curve resolution
Autor: Pedersen, Simon Vilms, Walther, Anders R., Callanan, Anthony, Stevens, Molly M., Hedegaard, Martin A. B., Arnspang, Eva C.
Unsupervised estimation of the dimensionality of hyperspectral microspectroscopy datasets containing pure and mixed spectral features, and extraction of their representative endmember spectra, remains a challenge in biochemical data mining. We report
Externí odkaz: http://arxiv.org/abs/2210.03238
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