26 papers · ranked by Valyu relevance
Runqing Wang, Qiguo Dai, Xiaodong Duan, Quan Zou
Advancements in spatial transcriptomics (ST) technology have enabled the analysis of gene expression while preserving cellular spatial information, greatly enhancing our understanding of cellular interactions within tissues. Accurate identification of spatial domains is crucial for comprehending tissue organization.…
Chen Qiao, Yuanhua Huang
Title: Summary Imputation of missing features in spatial transcriptomics is urgently needed due to technological limitations. However, most existing computational methods suffer from moderate accuracy and cannot estimate the reliability of the imputation. To fill this research gap, we introduce a computational model…
Ali Punjani, Haowei Zhang, David J. Fleet
Single particle cryo-EM is a powerful method for studying proteins and other biological macromolecules. Many of these molecules comprise regions with varying structural properties including disorder, flexibility, and partial occupancy. These traits make computational 3D reconstruction from 2D images challenging.…
Junyu Chen, Shuwen Wei, Yihao Liu, Zhangxing Bian + 4 more
diffeomorphic image registration Authors: ['Junyu Chen' 'Shuwen Wei' 'Yihao Liu' 'Zhangxing Bian' 'Yufan He' 'Aaron Carass' 'Harrison X. Bai' 'Yong Du'] Spatially varying regularization accommodates the deformation variations that may be necessary for different anatomical regions during deformable image registration.…
Xinyu Zhou, Pengtao Dang, Haixu Tang, Laura Xianlu Peng + 6 more
Spatial transcriptomics (ST) data demands models that recover how associations among molecular and cellular features change across tissue while contending with noise, collinearity, cell mixing, and thousands of predictors. We present Spatially Smooth Sparse Regression (S3R), a general framework that estimates…
Xinyu Zhou, Pengtao Dang, Xiao Wang, Laura Xianlu Peng + 7 more
Spatial transcriptomics (ST) data demands models that recover how associations among molecular and cellular features change across tissue while contending with noise, collinearity, cell mixing, and thousands of predictors. We present Spatially Smooth Sparse Regression (S3R), a general statistical framework that…
Éric Thiébaut, Ferréol Soulez, Laurent Denis
Optical interferometers provide multiple wavelength measurements. In order to fully exploit the spectral and spatial resolution of these instruments, new algorithms for image reconstruction have to be developed. Early attempts to deal with multi-chromatic interferometric data have consisted in recovering a gray image…
Tiantian Xu, Yuanjing Feng, Ye Wu, Qingrun Zeng + 4 more
'Jianzhong He' 'Qichuan Zhuge' 'Pew-Thian Yap'] Diffusion-weighted magnetic resonance imaging is a non-invasive imaging method that has been increasingly used in neuroscience imaging over the last decade. Partial volume effects (PVEs) exist in sampling signal for many physical and actual reasons, which lead to…
Jiasen Zhang, Weihong Guo, Zongwei Zhou, Tianming Liu
Deep learning methods have achieved outstanding results in many image processing and computer vision tasks, such as image segmentation. However, they usually do not consider spatial dependencies among pixels/voxels in the image. To obtain better results, some methods have been proposed to apply classic spatial…
Jiayu Su, Jean-Baptiste Reynier, Xi Fu, Guojie Zhong + 7 more
Spatial omics technologies can help identify spatially organized biological processes, but existing computational approaches often overlook structural dependencies in the data. Here, we introduce Smoother, a unified framework that integrates positional information into non-spatial models via modular priors and losses.…
Juan Liang, Jiuxi Huang, Chenxi Xi, Yun Wang + 2 more
The spatial transcriptomics technique provides an unprecedented perspective for analyzing the distribution patterns of cells within tissues and their functional tissue structures. To enhance the accuracy and robustness of spatial domain identification, we propose Joint Graph-Regularized Non-negative Matrix…
Matthew J. Heaton, Andrew Millane, Jake S. Rhodes
Spatial data display correlation between observations collected at neighboring locations. Generally, machine and deep learning methods either do not account for this correlation or do so indirectly through correlated features and thereby forfeit predictive accuracy. To remedy this shortcoming, we propose preprocessing…
Jussi Nurminen, Andrey Zhdanov, Wan Jin Yeo, Joonas Iivanainen + 5 more
'Julia Stephen' 'Amir Borna' 'Jim McKay' 'Peter D.D. Schwindt' 'Samu Taulu'] In magnetoencephalography, linear minimum norm inverse methods are commonly employed when a solution with minimal a priori assumptions is desirable. These methods typically produce spatially extended inverse solutions, even when the generating…
Anwar O. Nunez-Elizalde, Alexander G. Huth, Jack L. Gallant
Predictive models for neural or fMRI data are often fit using regression methods that employ priors on the model parameters. One widely used method is ridge regression, which employs a spherical Gaussian prior that assumes equal and independent variance for all parameters. However, a spherical prior is not always…
Bartłomiej W. Papież, James M. Franklin, Mattias P. Heinrich, Fergus V. Gleeson + 2 more
'Fergus V. Gleeson' 'Michael Brady' 'Julia A. Schnabel'] Title: Abstract. Deformable image registration, a key component of motion correction in medical imaging, needs to be efficient and provides plausible spatial transformations that reliably approximate biological aspects of complex human organ motion. Standard…
Hanno Reuvers, Etiënne Wijler
We consider a high-dimensional model in which variables are observed over time and space. The model consists of a spatio-temporal regression containing a time lag and a spatial lag of the dependent variable. Unlike classical spatial autoregressive models, we do not rely on a predetermined spatial interaction matrix…
Antti Aarnio, Olli Nykänen, Ville Kolehmainen, Mikko J. Nissi
5## DISCUSSION Subspace-constrained model utilizing spatial total variation and locally low-rank regularization, S-STV+LLR, was the best reconstruction model. The S-STV-LLR model was also the most sophisticated model and required tuning of two regularization parameters and choosing the appropriate subspace. While the…
Devis Tuia, Rémi Flamary, Michel Barlaud
— In this paper, we study the effect of different regularizers and their implications in high dimensional image classification and sparse linear unmixing. Although kernelization or sparse methods are globally accepted solutions for processing data in high dimensions, we present here a study on the impact of the form of…
Junqing Huang, Haihui Wang, Xuechao Wang, Michael Ruzhansky
—In this paper, we propose a semi-sparsity smoothing method based on a new sparsity-induced minimization scheme. The model is derived from the observations that semi-sparsity prior knowledge is universally applicable in situations where sparsity is not fully admitted such as in the polynomial-smoothing surfaces. We…
Hao Guo, André Python, Yu Liu
In spatial regression models, spatial heterogeneity may be considered with either continuous or discrete specifications. The latter is related to delineation of spatially connected regions with homogeneous relationships between variables (spatial regimes). Although various regionalization algorithms have been proposed…
Denis Tikhonov
Here, we present a new approach for obtaining radial distribution functions (RDF) from the electron diffraction data using a regularized weighted sine least-squares spectral analysis (rwsLSSA). It allows for explicitly transferring the measured experimental uncertainties in the reduced molecular scattering function to…
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…
Sanjar Adilov
Machine learning models for molecular-property prediction typically work with molecular representations in the form of fingerprints, descriptors, or graphs. In case of fingerprints and descriptors, molecular representations usually comprise thousands of features, which causes the curse of dimensionality for many…
Daniel Goldberg, Benjamin de Foy, M. Omar Nawaz, Jeremiah Johnson + 2 more
Air quality managers in areas exceeding air pollution standards are motivated to understand where there are further opportunities to reduce NOx emissions to improve ozone and PM2.5 air quality. In this project, we use a combination of aircraft remote sensing (i.e., GCAS), source apportionment models (i.e., CAMx), and…
Authors not listed
Plastic mechanical recycling is the conventional technological step towards circularity. In such aspects, complex mixtures of polyolefin blends are often fed into mechanical recycling systems, resulting in moulded products with uncertain quality. To add to the difficulty of heterogeneous feedstocks, the testing of…