Composition‐Aware Cross‐Sectional Integration for Spatial Transcriptomics
Multi‐section spatial transcriptomics demands coherent cell‐type deconvolution, domain detection, and batch correction, yet existing pipelines treat these tasks separately. FUSION unifies them within a composition‐aware latent framework, modeling reads as cell‐type–specific topics and clustering in embedding space.
Qishi Dong +5 more
wiley +1 more source
Environmental Drivers and Explainable Modeling to Resolve Trace Metal Dynamics in a Lotic System. [PDF]
Topçu A +3 more
europepmc +1 more source
High-Accuracy Wave Direction Estimation Using Kalman Fusion of Interferometric Measurements and Energy Field Reconstruction. [PDF]
Wang C, Li X, Xue L.
europepmc +1 more source
Fractional modeling of hepatitis B virus transmission via heterosexual and homosexual contacts and its disability burden. [PDF]
Guedri K +5 more
europepmc +1 more source
Discontinuity Characterization and Low-Complexity Smoothing in RF-PA Polynomial Piecewise Modeling. [PDF]
Pedrosa C +5 more
europepmc +1 more source
Validation of Novel Stride Length Model-Based Approaches to Estimate Distance Covered Based on Acceleration and Pressure Data During Walking. [PDF]
Ngueleu AM, Otis MJ, Batcho CS.
europepmc +1 more source
Investigation of Lacosamide solubility in supercritical carbon dioxide with machine learning models. [PDF]
Esfandiari N.
europepmc +1 more source

