Results 71 to 80 of about 159,702 (261)

cuteHap: Haplotype‐Aware Structural Variant Detection in Phased Long‐Read Sequencing Data

Advanced Science, EarlyView.
cuteHap is a haplotype‐aware structural variant detection method designed for phased long‐read sequencing. By employing self‐adaptive clustering and credibility‐prioritized beam search algorithms, cuteHap generates accurate haplotype‐resolved calls and outperforms state‐of‐the‐art tools.
Shuqi Cao, Yadong Liu, Miao Cui, Runtian Gao, Weimin Guo, Guohua Wang, Yadong Wang, Tao Jiang   +7 more
wiley   +1 more source

Learning a Flexible K-Dependence Bayesian Classifier from the Chain Rule of Joint Probability Distribution

Entropy, 2015
As one of the most common types of graphical models, the Bayesian classifier has become an extremely popular approach to dealing with uncertainty and complexity.
Limin Wang, Haoyu Zhao
doaj   +1 more source

Pan‐3D Genome Analysis Reveals the Roles of Structural Variation in Chicken Chromatin Architectures, Domestication and Production Traits

Advanced Science, EarlyView.
This study firstly presents a comprehensive and high‐resolution pan‐3D genome resource in chicken. Our findings reveal the role of structural variations in 3D genome architectures, and how they influence the domestication process and production traits at the 3D genome level.
Zhen Zhou, Danfeng Cai, Changbin Zhao, Siyu Zhang, Jiahao Li, Zhaofeng Zhang, Shaofen Kong, Xin Yang, Xiaoli Zhou, Farhad Bordbar, Fayi Chen, Yaohuan Xu, Zhe Zhang, Lihong Gu, Zhenhui Li, Xiquan Zhang, Wen Luo, Jingting Shu, Bolin Cai, Qinghua Nie   +19 more
wiley   +1 more source

Subjective confidence reflects representation of Bayesian probability in cortex. [PDF]

Nat Hum Behav, 2022
Geurts LS, Cooke JRH, van Bergen RS, Jehee JFM.   +3 more
europepmc   +1 more source

Being Bayesian about Categorical Probability

, 2020
Neural networks utilize the softmax as a building block in classification tasks, which contains an overconfidence problem and lacks an uncertainty representation ability. As a Bayesian alternative to the softmax, we consider a random variable of a categorical probability over class labels. In this framework, the prior distribution explicitly models the
Joo, Taejong, Chung, Uijung, Seo, Min-Gwan   +2 more
openaire   +2 more sources

Reconstructing Coherent Functional Landscape From Multi‐Modal Multi‐Slice Spatial Transcriptomics by a Variational Spatial Gaussian Process

Advanced Science, EarlyView.
This study introduces stVGP, a variational spatial Gaussian process framework for multi‐modal, multi‐slice spatial transcriptomics. By integrating histological and genomic data through hybrid alignment and attention‐based fusion, stVGP reconstructs coherent 3D functional landscapes.
Zedong Wang, Bowen Fu, Chuanchao Zhang, Xiaoping Liu   +3 more
wiley   +1 more source

Raman spectroscopy and artificial intelligence to predict the Bayesian probability of breast cancer. [PDF]

Sci Rep, 2021
Kothari R, Jones V, Mena D, Bermúdez Reyes V, Shon Y, Smith JP, Schmolze D, Cha PD, Lai L, Fong Y, Storrie-Lombardi MC.   +10 more
europepmc   +1 more source

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

A Scalable Framework for Comprehensive Typing of Polymorphic Immune Genes from Long‐Read Data

Advanced Science, EarlyView.
SpecImmune introduces a unified computational framework optimized for long‐read sequencing to resolve over 400 highly polymorphic immune genes. This scalable approach achieves high‐resolution typing, enabling the discovery of cross‐family co‐evolutionary networks and population‐specific diversity.
Shuai Wang, Xuedong Wang, Mengyao Wang, Qian Zhou, Lusheng Wang, Shuai Cheng Li   +5 more
wiley   +1 more source

A Note of Caution on Maximizing Entropy

Entropy, 2014
The Principle of Maximum Entropy is often used to update probabilities due to evidence instead of performing Bayesian updating using Bayes’ Theorem, and its use often has efficacious results.
Richard E. Neapolitan, Xia Jiang
doaj   +1 more source