25 papers · ranked by Valyu relevance
Yi Wang, Zihang He, Yunjie Yan
Multimodal spatial omics enables systematic characterization of tissue organization by jointly profiling molecular features across transcriptomic, proteomic, and metabolomic layers. However, integrative analysis across sections and modalities is frequently hindered by non-linear tissue distortions, mismatched spatial…
Sergiy Popovych, Thomas Macrina, Nico Kemnitz, Manuel Castro + 9 more
The reconstruction of neural circuits from serial section electron microscopy (ssEM) images is being accelerated by automatic image segmentation methods. Segmentation accuracy is often limited by the preceding step of aligning 2D section images to create a 3D image stack. Precise and robust alignment in the presence of…
Yecheng Tan, Zezhou Wang, Ai Wang, Yan Yan + 3 more
Spatial transcriptomics (ST) technologies offer rich spatial context for gene expression, with varying spatial resolutions and gene coverages. However, aligning and comparing multiple ST slices, whether derived from the same or different platforms, remains challenging due to nonlinear distortions and limited spatial…
Yidong Shen, Li Luo, Guoqing Wang, Tao Liu + 3 more
Due to the influence of the complex underwater environment, the initial alignment method for Doppler velocity log (DVL)-aided strap-down inertial navigation systems (SINS) often suffer from performance degradation, especially when DVL measurements are contaminated by outliers. In this paper, an outlier-resistant…
Donatas Sederevičius, Atle Bjørnerud, Kristine B. Walhovd, Koen Van Leemput + 2 more
Variations in image intensities between magnetic resonance imaging (MRI) acquisitions affect the subsequent image processing and its derived outcomes. Therefore, it is necessary to normalize images of different scanners/acquisitions, especially for longitudinal studies where a change of scanner or pulse sequence often…
Yue Hu
Protein structure alignment is a fundamental task in computational biology. While the TM-score is a gold standard for evaluating structural similarity, its direct optimization is challenging. Many algorithms, therefore, rely on heuristic or combinatorial approaches. In this work, we present OTMalign, a novel algorithm…
Xianyun Qian, Fei Wen, Peilin Liu, Toon Goedemé
Robust 3D registration is a fundamental problem in computer vision and robotics, where the goal is to estimate the geometric transformation between two sets of measurements in the presence of noise and outlier contamination. Existing robust registration methods are mainly built on either maximum consensus (MC)…
Yue Hu, Zanxia Cao, Yingchao Liu
The Root-Mean-Square Deviation (RMSD) metric, coupled with the Kabsch algorithm for optimal superposition, represents a cornerstone of quantitative structural biology. However, its application is fundamentally limited to the comparison of structures with a pre-defined, one-to-one correspondence of equal length…
Tallon Coxe, David J. Burks, Utkarsh Singh, Ron Mittler + 2 more
The utmost goal of selecting an RNA-Seq alignment software is to perform accurate alignments with a robust algorithm, which is capable of detecting the various intricacies underlying read-mapping procedures and beyond. Most alignment software tools are typically pre-tuned with human or prokaryotic data, and therefore…
Michael Adlerstein, João Carlos Virgolino Soares, Angelo Bratta, Claudio Semini
'Claudio Semini'] Abstract— Point cloud registration is a critical problem in computer vision and robotics, especially in the field of navigation. Current methods often fail when faced with high outlier rates or take a long time to converge to a suitable solution. In this work, we introduce a novel algorithm for point…
Lingjie Su, Wei Xu, Shuyang Zhao, Yuqi Cheng + 1 more
Clouds Considering Local Consistency Authors: ['Lingjie Su' 'Wei Xu' 'Shuyang Zhao' 'Yuqi Cheng' 'Wenlong Li'] Abstract— In robotic inspection, joint registration of multiple point clouds is an essential technique for estimating the transformation relationships between measured parts, such as multiple blades in a…
Anila Johnson, Umashankar Subramaniam, HyungSeok Kim, Divya Udayan J + 1 more
The significance of three-dimensional point cloud data in generating high precision point cloud maps for environmental sensing, geo-spatial analysis and autonomous driving is increasing today with the advancements in sensing technologies such as LiDAR. Accurate registration is required to reduce noise and maintain…
Yael Harpaz, Yoel Shkolnisky
A common task in cryo-electron microscopy data processing is to compare three-dimensional density maps of macromolecules. In this paper, we propose an algorithm for aligning three-dimensional density maps, which exploits common lines between projection images of the maps. The algorithm is fully automatic and handles…
Authors not listed
In molecular machine learning, the choice of the representation of molecules can have a significant impact on model performance. However, understanding the root causes of these performance differences often proves challenging. One promising approach to explore model behavior is representational alignment, which…
Seong Hun Lee, Javier Civera
— In this work, we propose a novel method for robust single rotation averaging that can efficiently handle an extremely large fraction of outliers. Our approach is to minimize the total truncated least unsquared deviations (TLUD) cost of geodesic distances. The proposed algorithm consists of three steps: First, we…
Michael Gentner, Prajval Kumar Murali, Mohsen Kaboli
— Point cloud registration is a fundamental and challenging problem for autonomous robots interacting in unstructured environments for applications such as object pose estimation, simultaneous localization and mapping, robotsensor calibration, and so on. In global correspondence-based point cloud registration, data…
Enwen Hu, Lei Sun
—Correspondence-based point cloud registration is a cornerstone in geometric computer vision, robotics perception, photogrammetry and remote sensing, which seeks to estimate the best rigid transformation between two point clouds from the correspondences established over 3D keypoints. However, due to limited robustness…
Tianyu Huang, Haoang Li, Liangzu Peng, Yinlong Liu + 1 more
Parameter Search Authors: ['Tianyu Huang' 'Haoang Li' 'Liangzu Peng' 'Yinlong Liu' 'Yunhui Liu'] Abstract—Estimating the rigid transformation with 6 degrees of freedom based on a putative 3D correspondence set is a crucial procedure in point cloud registration. Existing correspondence identification methods usually…
Rasha Atwi, Ye Wang, Simone Sciabola, Adam Antoszewski
Efficient virtual screening techniques are critical in drug discovery for identifying potential drug candidates. We present an open-source package for molecular alignment and 3D similarity calculations optimized for large-scale virtual screening of small molecules. This work parallels widely used proprietary tools and…
Tiancheng Li, Peter Walker, Dima A. Hammoud, Liang Zhao + 1 more
Orthopedic Surgery Authors: ['Tiancheng Li' 'Peter Walker' 'Dima A. Hammoud' 'Liang Zhao' 'Shoudong Huang'] Abstract—In computer-assisted orthopedic surgery (CAOS), accurate pre-operative to intra-operative bone registration is an essential and critical requirement for providing navigational guidance. This registration…
Marjan Hadian-Jazi, Alireza Sadri, D. G. Waterman
This article introduces RGFlib, a Python package for robust statistical analysis. The package is a useful tool for a variety of tasks in X-ray crystallography data analysis, such as peak-finding, bad pixel mask making and other outlier-detection tasks.
Miquel Anglada-Girotto, Samuel Miravet-Verde, Luis Serrano, Sarah A. Head
Independent Component Analysis (ICA) allows the dissection of omic datasets into modules that help to interpret global molecular signatures. The inherent randomness of this algorithm can be overcome by clustering many iterations of ICA together to obtain robust components. Existing algorithms for robust ICA are…
Authors not listed
Machine learning models are increasingly applied to heterogeneous materials datasets spanning different synthesis routes, measurement protocols, and structural classes. Although multi-task and representation-learning approaches are commonly used to improve predictive performance, the latent representations learned by…
Dimitris Gkoumas, Maria Liakata
The intersection of chemistry and Artificial Intelligence (AI) is an active area of research focused on accelerating scientific discovery. While using large language models (LLMs) with scientific modalities has shown potential, there are significant challenges to address, such as improving training efficiency and…
Joseph Redshaw, Darren Ting, Alex Brown, Jonathan Hirst + 1 more
Antimicrobial peptides (AMPs) represent a potential solution to the growing problem of antimicrobial resistance, yet their identification through wet-lab experiments is a costly and timeconsuming process. Accurate computational predictions would allow rapid in silico screening of candidate AMPs, thereby accelerating…