Results 71 to 80 of about 425,037 (266)

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

Exact Inference with Approximate Computation for Differentially Private Data via Perturbations

The Journal of Privacy and Confidentiality, 2022
This paper discusses how two classes of approximate computation algorithms can be adapted, in a modular fashion, to achieve exact statistical inference from differentially private data products.
Ruobin Gong
doaj  

On econometric inference and multiple use of the same data

, 2015
In fields that are mainly nonexperimental, such as economics and finance, it is inescapable to compute test statistics and confidence regions that are not probabilistically independent from previously examined data.
Grønneberg, Steffen, Holcblat, Benjamin
core   +1 more source

Natural Variation of NAR5 Determines Nitrogenase Activity and the Yield in Soybean

Advanced Science, EarlyView.
This study identified NAR5, a gene encoding a subtilisin‐like protease, that regulates nitrogenase activity in soybean nodules. Overexpressing NAR5 delayed nodule senescence, enhancing nitrogenase activity, yield, and low‐nitrogen tolerance. The elite haplotype NAR5HapI‐1 linked to superior nitrogenase activity and greater seed weight has been ...
Chao Ma, Hanyu Zhao, Haojie Feng, Xulun Dong, Lin Chen, Mingliang Yang, Runyi Chen, Chengjun Lei, Chunyan Liu, Qingshan Chen, Dawei Xin, Jinhui Wang   +11 more
wiley   +1 more source

MIXED-EFFECT MODELS WITH RESTRICTED MAXIMUM LIKELIHOOD (REML), BOOT-STRAPPED REML AND BAYESIAN INFERENCE IN APPLICATION OF GAPMINDER DATA

Barekeng
Mixed effects model combines fixed effects and random effects, allowing for the analysis of data with both fixed and random variations. This modeling approach is widely utilized across various fields.
Asysta Amalia Pasaribu, Kusman Sadik, Anang Kurnia   +2 more
doaj   +1 more source

Variational Bayesian Sparse Signal Recovery With LSM Prior

IEEE Access, 2017
This paper presents a new sparse signal recovery algorithm using variational Bayesian inference based on the Laplace approximation. The sparse signal is modeled as the Laplacian scale mixture (LSM) prior.
Shuanghui Zhang, Yongxiang Liu, Xiang Li, Guoan Bi   +3 more
doaj   +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

Physics‐Embedded Neural Network: A Novel Approach to Design Polymeric Materials

Advanced Science, EarlyView.
Traditional black‐box models for polymer mechanics rely solely on data and lack physical interpretability. This work presents a physics‐embedded neural network (PENN) that integrates constitutive equations into machine learning. The approach ensures reliable stress predictions, provides interpretable parameters, and enables performance‐driven, inverse ...
Siqi Zhan, Hengheng Zhao, Haotian Wang, Zhanjie Liu, Qian Li, Weifeng Zhang, Qingsong Zhao, Liqun Zhang, Jun Liu   +8 more
wiley   +1 more source

Bayesian Methodologies with pyhf [PDF]

EPJ Web of Conferences
bayesian_pyhf is a Python package that allows for the parallel Bayesian and frequentist evaluation of multi-channel binned statistical models. The Python library pyhf is used to build such models according to the HistFactory framework and already ...
Feickert Matthew, Heinrich Lukas, Horstmann Malin   +2 more
doaj   +1 more source

Bayesian quantile regression: An application to the wage distribution in 1990s Britain [PDF]

This paper illustrates application of Bayesian inference to quantile regression. Bayesian inference regards unknown parameters as random variables, and we describe an MCMC algorithm to estimate the posterior densities of quantile regression parameters ...
Van Kerm, Philippe, Yu, Keming, Zhang, Jin   +2 more
core