Results 61 to 70 of about 114,074 (272)

SpatialESD: Spatial Ensemble Domain Detection in Spatial Transcriptomics

Advanced Science, EarlyView.
ABSTRACT Spatial transcriptomics (ST) measures gene expression while preserving spatial context within tissues. One of the key tasks in ST analysis is spatial domain detection, which remains challenging due to the complex structure of ST data and the varying performance of individual clustering methods. To address this, we propose SpatialESD, a Spatial
Hongyan Cao, Gaiqin Liu, Jingyi Xia, Runle Chen, Tong Wang, Xiaoling Yang, Ruiling Fang, Yanhong Luo, Ping Zeng, Hongmei Yu, Yanbo Zhang, Yuehua Cui   +11 more
wiley   +1 more source

Leveraging Artificial Intelligence and Large Language Models for Cancer Immunotherapy

Advanced Science, EarlyView.
Cancer immunotherapy faces challenges in predicting treatment responses and understanding resistance mechanisms. Artificial intelligence (AI) and machine learning (ML) offer powerful solutions for cancer immunotherapy in patient stratification, biomarker discovery, treatment strategy optimization, and foundation model development.
Xinchao Wu, Mengtao Sun, Lusheng Li, Jieqiong Wang, Shibiao Wan   +4 more
wiley   +1 more source

Super-Resolution Ultrasound Imaging by Sparse Bayesian Learning Method

IEEE Access, 2019
Super-resolution ultrasound (SR-US) imaging technique overcomes the acoustic diffraction limit and greatly improves the spatial resolution. Furthermore, by exploiting temporal fluctuations in microbubbles, a super-resolution fluctuation imaging (SOFI ...
Ying Liu, Yi Yang, Yuexia Shu, Tianyang Zhou, Jianwen Luo, Xin Liu   +5 more
doaj   +1 more source

Two-Dimensional Pattern-Coupled Sparse Bayesian Learning via Generalized Approximate Message Passing

, 2015
We consider the problem of recovering two-dimensional (2-D) block-sparse signals with \emph{unknown} cluster patterns. Two-dimensional block-sparse patterns arise naturally in many practical applications such as foreground detection and inverse synthetic
Fang, Jun, Li, Hongbin, Zhang, Lizao
core   +1 more source

Sparse Bayesian Learning for Directions of Arrival on an FPGA [PDF]

2018 IEEE Statistical Signal Processing Workshop (SSP), 2018
A direction of arrival (DOA) estimator based on sparse Bayesian learning (SBL) is implemented as a fixed-point arithmetic prototype for an FPGA platform. The prototype is developed from a known algorithm mainly using high-level synthesis with C++ based model specifications. The specialized equations of the algorithm are reduced to arithmetic operations
Herbert Groll, Christoph F. Mecklenbräuker, Peter Gerstoft   +2 more
openaire   +1 more source

ML Workflows for Screening Degradation‐Relevant Properties of Forever Chemicals

Advanced Science, EarlyView.
The environmental persistence of per‐ and polyfluoroalkyl substances (PFAS) necessitates efficient remediation strategies. This study presents physics‐informed machine learning workflows that accurately predict critical degradation properties, including bond dissociation energies and polarizability.
Pranoy Ray, Andrew R. Castillo, Manoj Kolel‐Veetil, Surya R. Kalidindi   +3 more
wiley   +1 more source

Frequency-difference sparse Bayesian learning for unambiguous direction-of-arrival estimation [PDF]

JASA Express Letters
The frequency-difference (FD) method uses the FD Hadamard product, comprising auto-products to model below-band acoustic fields and unintended cross-products, for efficient direction-of-arrival (DOA) estimation under spatial aliasing.
Ze Yuan, Haiqiang Niu, Zhenglin Li, Wenyu Luo   +3 more
doaj   +1 more source

Sustainable Materials Design With Multi‐Modal Artificial Intelligence

Advanced Science, EarlyView.
Critical mineral scarcity, high embodied carbon, and persistent pollution from materials processing intensify the need for sustainable materials design. This review frames the problem as multi‐objective optimization under heterogeneous, high‐dimensional evidence and highlights multi‐modal AI as an enabling pathway.
Tianyi Xu, Tianshuo Wei, Yan Ge, Bo Peng, Yue Li, Maolin Wang, Peng Wen, Chao Yang, Ye Wei   +8 more
wiley   +1 more source

Compound Poisson Processes, Latent Shrinkage Priors and Bayesian Nonconvex Penalization

, 2015
In this paper we discuss Bayesian nonconvex penalization for sparse learning problems. We explore a nonparametric formulation for latent shrinkage parameters using subordinators which are one-dimensional L\'{e}vy processes. We particularly study a family
Li, Jin, Zhang, Zhihua
core   +1 more source

STAID: A Self‐Refining Deep Learning Framework for Spatial Cell‐Type Deconvolution with Biologically Informed Modeling

Advanced Science, EarlyView.
STAID is a unified deep learning framework that couples iterative pseudo‐spot refinement with neural network training through a feedback loop and exploits gene co‐expression information to model higher‐order interactions, achieving accurate and robust cell‐type deconvolution in spatial transcriptomics.
Jixin Liu, Shuli Sun, Zhengliang Lv, Xinyu Liu, Yihua Wang, Bingqiang Liu   +5 more
wiley   +1 more source