Results 41 to 50 of about 2,237,660 (304)
A new integrated framework for the identification of potential virus–drug associations
Frontiers in Microbiology, 2023 IntroductionWith the increasingly serious problem of antiviral drug resistance, drug repurposing offers a time-efficient and cost-effective way to find potential therapeutic agents for disease.
Jia Qu +4 more
doaj +1 more source
A Generalized CUR Decomposition for Matrix Pairs
SIAM Journal on Mathematics of Data Science, 2022 We propose a generalized CUR (GCUR) decomposition for matrix pairs $(A, B)$. Given matrices $A$ and $B$ with the same number of columns, such a decomposition provides low-rank approximations of both matrices simultaneously, in terms of some of their rows and columns.
Perfect Y. Gidisu +1 more
openaire +6 more sources
Elevated Connectivity During Language Processing Is Associated With Cognitive Performance in SeLECTS
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Self‐Limited Epilepsy with Centrotemporal Spikes (SeLECTS) is associated with language impairments despite seizures originating in the motor cortex, suggesting aberrant cross‐network interactions. Here we tested whether functional connectivity in SeLECTS during language tasks predicts language performance.
Wendy Qi +8 more
wiley +1 more source
Matrix Decomposition and Applications [PDF]
, 2023 In 1954, Alston S. Householder published Principles of Numerical Analysis, one of the first modern treatments on matrix decomposition that favored a (block) LU decomposition-the factorization of a matrix into the product of lower and upper triangular ...
Lu, Jun
core +1 more source
Learnable Graph-Regularization for Matrix Decomposition
ACM Transactions on Knowledge Discovery from Data, 2023 Low-rank approximation models of data matrices have become important machine learning and data mining tools in many fields, including computer vision, text mining, bioinformatics, and many others. They allow for embedding high-dimensional data into low-dimensional spaces, which mitigates the effects of noise and uncovers latent relations.
Penglong Zhai, Shihua Zhang
openaire +3 more sources
Deep Learning Pose Estimation for Phenotyping of Co‐Occurring Hyperkinetic Movement Disorders
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective To explore whether routine outpatient video combined with deep learning‐based pose estimation and clinically interpretable kinematic features can support multi‐label phenotyping of co‐occurring hyperkinetic movement disorders (HMDs).
Laura Cif +17 more
wiley +1 more source
The Journal of Engineering, 2017 Based on the vectorised and cache optimised kernel, a parallel lower upper decomposition with a novel communication avoiding pivoting scheme is developed to solve dense complex matrix equations generated by the method of moments.
Yan Chen +4 more
doaj +1 more source
Guided Semi-Supervised Non-Negative Matrix Factorization
Algorithms, 2022 Classification and topic modeling are popular techniques in machine learning that extract information from large-scale datasets. By incorporating a priori information such as labels or important features, methods have been developed to perform ...
Pengyu Li +6 more
doaj +1 more source
Natural Frequencies of Levodopa‐Induced Dyskinesia in Parkinson's Disease
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objectives Abnormal involuntary movements, known as dyskinesias, are common complications of levodopa treatment in patients with Parkinson's disease and can significantly impair quality of life. The underlying pathophysiology remains unclear, and current therapeutic options are limited.
Ioannis U. Isaias +3 more
wiley +1 more source