21 papers · ranked by Valyu relevance
Gabriel Torregrosa, David Oriola, Vikas Trivedi, Jordi Garcia-Ojalvo
Flow cytometry enables monitoring protein abundance and activity at the single-cell level in a high-throughput manner, through the use of fluorescent labeling. Given the significant levels of autofluorescence emitted by cells at the spectral ranges used by this technique, removing the corresponding background signal is…
Zachary H. Hendrix, Peter Brown, Tim Flanagan, Douglas P. Shepherd + 2 more
Motivated Bayesian Deconvolution Authors: ['Zachary H. Hendrix' 'Peter Brown' 'Tim Flanagan' 'Douglas P. Shepherd' 'Ayush Saurabh' 'Steve Pressé'] Richardson-Lucy deconvolution is widely used to restore images from degradation caused by the broadening effects of a point spread function and corruption by photon shot…
Guillermina Senn, Håkon Tjelmeland, Nathan Glatt-Holtz, Matt Walker + 1 more
Blind image deconvolution refers to the problem of simultaneously estimating the blur kernel and the true image from a set of observations when both the blur kernel and the true image are unknown. Sometimes, additional image and/or blur information is available and the term semi-blind deconvolution (SBD) is used. We…
Gabriel Torregrosa-Cortés, David Oriola, Vikas Trivedi, Jordi Garcia-Ojalvo
'Jordi Garcia-Ojalvo'] Title: Summary Individual cells exhibit substantial heterogeneity in protein abundance and activity, which is frequently reflected in broad distributions of fluorescently labeled reporters. Since all cellular components are intrinsically fluorescent to some extent, the observed distributions…
Saba Ghaffari, Kelly J. Bouchonville, Ehsan Saleh, Remington E. Schmidt + 2 more
Differential gene expression in bulk transcriptomics data can reflect change of transcript abundance within a cell type and/or change in the proportion of cell types within the sample. Expression deconvolution methods can help differentiate these scenarios and enable more accurate inference of gene regulation by…
Parker S. Ruth, Tommy DeBenedetti, Lily O’Brien, James A. Landay + 2 more
Vascular waveforms, which measure bulk flow in blood vessels, are widely used to measure vital signs, diagnose conditions, and predict long-term health outcomes. Analyzing vascular waveforms depends on three fundamentally interdependent tasks: signal filtering, pulse timing detection, and pulse shape extraction. We…
Felipe Tobar, A. Robert, Jorge F. Silva
Let us consider the deconvolution problem, that is, to recover a latent source x(·) from the observations y = [y1, . . . , yN ] of a convolution process y = x ? h + η, where η is an additive noise, the observations in y might have missing parts with respect to y, and the filter h could be unknown. We propose a novel…
Mengying Hu, Maria Chikina
Computational cell-type deconvolution is an important analytic technique for modeling the compositional heterogeneity of bulk gene expression data. A conceptually new Bayesian approach to this problem, BayesPrism, has recently been proposed and has subsequently been shown to be superior in accuracy and robustness…
Minh-Hai Nguyen, Edouard Pauwels, Pierre Weiss
The Maximum A Posteriori (MAP) estimation is a widely used framework in blind deconvolution to recover sharp images from blurred observations. The estimated image and blur filter are defined as the maximizer of the posterior distribution. However, when paired with sparsity-promoting image priors, MAP estimation has…
Authors not listed
Electrochemical impedance spectroscopy (EIS) coupled with distribution of relaxation times (DRT) analysis is a robust framework for characterizing electrochemical systems. However, DRT deconvolution is often plagued by spurious peaks, hindering accurate process identification and quantitative parameter estimation. To…
Adeleke Maradesa, Baptiste Py, Ting Hei Wan, Mohammed B. Effat + 1 more
Electrochemical impedance spectroscopy (EIS) is a characterization technique used widely in electrochemistry. Obtaining EIS data is simple when modern electrochemical workstations are used; however, analyzing EIS spectra is still a considerable quandary. The distribution of relaxation times (DRT) has emerged as a…
Konstantinos Bakas, John Kornak, Hernando Ombao
Bayesian methods are commonly applied to solve image analysis problems such as noise-reduction, feature enhancement and object detection. A primary limitation of these approaches is the computational complexity due to the interdependence of neighboring pixels which limits the ability to perform full posterior sampling…
Elhadji C. Faye, Mame Diarra Fall, Nicolas Dobigeon
—This paper introduces a Bayesian framework for image inversion by deriving a probabilistic counterpart to the regularization-by-denoising (RED) paradigm. It additionally implements a Monte Carlo algorithm specifically tailored for sampling from the resulting posterior distribution, based on an asymptotically exact…
Oscar Bates, Lluis Guasch, George Strong, Thomas Caradoc Robins + 4 more
'Oscar Calderon-Agudo' 'Carlos Cueto' 'Javier Cudeiro' 'Mengxing Tang'] Bayesian methods are a popular research direction for inverse problems. There are a variety of techniques available to solve Bayes’ equation, each with their own strengths and limitations. Here, we discuss stochastic variational inference (SVI)…
Emanuele Quattrocchi, Baptiste Py, Adeleke Maradesa, Quentin Meyer + 2 more
Electrochemical impedance spectroscopy (EIS) is a characterization technique widely used to evaluate the properties of electrochemical systems. The distribution of relaxation times (DRT) has emerged as a model-free alternative to equivalent circuits and physical models to circumvent the inherent challenges of EIS…
Jürgen Köfinger, Gerhard Hummer
The proper balancing of information from experiment and theory is a long-standing problem in the analysis of noisy and incomplete data. Viewed as a Pareto optimization problem, improved agreement with the experimental data comes at the expense of growing inconsistencies with the theoretical reference model. Here, we…
Authors not listed
The political, social and economic consequences of climate change drastically influence the requirements of modern energy systems and its components. This includes not only energy production but also concepts and innovations for its storage, especially in magnitudes of gigawatt hours. Carnot batteries, which convert…
Denice van Herwerden, Jake O'Brien, Sascha Lege, Bob Pirok + 2 more
Fragment deconvolution is a crucial step during componentization of non-targeted analysis (NTA) high-resolution mass spectrometry (HRMS) data, aiming to filter out false positive (FP) signals that do not belong to the component. Moreover, inclusion of FP fragments could lead to, for example, wrong identification…
Brandon S Coventry, Edward L Bartlett
Typical statistical practices in the biological sciences have been increasingly called into question due to difficulties in replication of an increasing number of studies, many of which are confounded by the relative difficulty of null significance hypothesis testing designs and interpretation of p-values. Bayesian…
Brandon S. Coventry, Edward L. Bartlett
Typical statistical practices in the biological sciences have been increasingly called into question due to difficulties in the replication of an increasing number of studies, many of which are confounded by the relative difficulty of null significance hypothesis testing designs and interpretation of p-values. Bayesian…
Matthew Stephens
I discuss the benefits of looking through the ‘Bayesian lens’ (seeking a Bayesian interpretation of ostensibly non-Bayesian methods), and the dangers of wearing ‘Bayesian blinkers’ (eschewing non-Bayesian methods as a matter of philosophical principle). I hope that the ideas may be useful to scientists trying to…