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| http://dx.doi.org/10.3389/fgene.2014.00052 | DOI Listing |
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Point spread function estimation with computed wavefronts for deconvolution of hyperspectral imaging data.
Sci Rep
January 2025
Hannover Centre for Optical Technologies (HOT), Leibniz University Hannover, Hannover, Germany.
Hyperspectral imaging (HSI) systems acquire images with spectral information over a wide range of wavelengths but are often affected by chromatic and other optical aberrations that degrade image quality. Deconvolution algorithms can improve the spatial resolution of HSI systems, yet retrieving the point spread function (PSF) is a crucial and challenging step. To address this challenge, we have developed a method for PSF estimation in HSI systems based on computed wavefronts.
View Article and Find Full Text PDFOnline-adjusted evolutionary biclustering algorithm to identify significant modules in gene expression data.
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November 2024
Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México, Circuito Escolar, Ciudad Universitaria, 04510 Mexico city, México.
Analyzing gene expression data helps the identification of significant biological relationships in genes. With a growing number of open biological datasets available, it is paramount to use reliable and innovative methods to perform in-depth analyses of biological data and ensure that informed decisions are made based on accurate information. Evolutionary algorithms have been successful in the analysis of biological datasets.
View Article and Find Full Text PDFEmergence of structures in neuronal network activities.
Sci Rep
January 2025
Department of Medicine (Neurology), McMaster University, Hamilton, ON, L8L 2X2, Canada.
Nonlinear responses of individual neurons are both experimentally established and considered fundamental for the functioning of neuronal circuitry. Consequently, one may envisage the collective dynamics of large networks of neurons exhibiting a large repertoire of nonlinear behaviors. However, an ongoing and central challenge in the modeling of neural dynamics involves the trade-off between tractability and biological realism.
View Article and Find Full Text PDFA New Perspective in Epilepsy Classification: Applying the Taxonomy of Seizure Dynamotypes to Non-Invasive EEG and examining dynamical changes across sleep stages.
eNeuro
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Department of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel. Department of Computer Science, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
Epilepsy, a neurological disorder characterized by recurrent unprovoked seizures, significantly impacts patient quality of life. Current classification methods focus primarily on clinical observations and electroencephalography (EEG) analysis, often overlooking the underlying dynamics driving seizures. This study uses surface EEG data to identify seizure transitions using a dynamical systems-based framework-the taxonomy of seizure dynamotypes-previously examined only in invasive data.
View Article and Find Full Text PDFStructured feature ranking for genomic marker identification accommodating multiple types of networks.
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October 2024
Department of Urology, Xinhua Hospital affiliated to Shanghai Jiao Tong University School of Medicine, 1665 Kongjiang Road, Shanghai 200092, China.
Numerous statistical methods have been developed to search for genomic markers associated with the development, progression, and response to treatment of complex diseases. Among them, feature ranking plays a vital role due to its intuitive formulation and computational efficiency. However, most of the existing methods are based on the marginal importance of molecular predictors and share the limitation that the dependence (network) structures among predictors are not well accommodated, where a disease phenotype usually reflects various biological processes that interact in a complex network.
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