Results 71 to 80 of about 436,673 (317)

Multi‐Tissue Genetic Regulation of RNA Editing in Pigs

Advanced Science, EarlyView.
This study presents the first multi‐tissue map of RNA editing and its genetic regulation in pigs. By integrating RNA editing profiles, edQTL mapping, GWAS, and cross‐species comparisons, this work establishes RNA editing as a distinct regulatory layer linking genetic variation to complex traits, highlighting its functional and evolutionary significance.
Xiangchun Pan, Wentao Gong, Xiaodian Cai, Jinyan Teng, Jiali Cai, Haonan Zeng, Wondossen Ayalew, Qingpeng Shen, Zhanming Zhong, Yifei Wang, Wenjing Zhang, Yuhan Tian, Dantong Xu, Yahui Gao, Hongwei Yin, Yuebo Zhang, Jiahui Hou, Tianru Zhou, Jiaqi Li, Lingzhao Fang, Xiaolong Yuan, Zhe Zhang   +21 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  

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

Automatic Variational Inference in Stan [PDF]

, 2015
Variational inference is a scalable technique for approximate Bayesian inference. Deriving variational inference algorithms requires tedious model-specific calculations; this makes it difficult to automate.
Blei, David M., Gelman, Andrew, Kucukelbir, Alp, Ranganath, Rajesh   +3 more
core  

Sparse Stochastic Inference for Latent Dirichlet allocation

, 2012
We present a hybrid algorithm for Bayesian topic models that combines the efficiency of sparse Gibbs sampling with the scalability of online stochastic inference.
Blei, David, Hoffman, Matt, Mimno, David
core   +2 more sources

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

Noise Fingerprints as a Quantitative Order Parameter for Polarization‐ and Defect‐Mediated Switching in Hafnia Ferroelectrics

Advanced Science, EarlyView.
Low‐frequency noise fingerprints in hafnia ferroelectrics provide a quantitative handle to resolve the long‐standing debate between polarization‐mediated and defect‐mediated switching. By tuning oxygen vacancy density via ALD O3 dose time and applying a physically constrained deconvolution, we extract bias‐resolved current fractions for both mechanisms
Ryun‐Han Koo, Jiseong Im, Joon Hwang, Sung‐Ho Park, Jonghyun Ko, Kangwook Choi, Sangwoo Ryu, Gyuweon Jung, Jong‐Ho Lee   +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

Kernel-based Approximate Bayesian Inference for Exponential Family Random Graph Models [PDF]

, 2020
Fan Yin, Carter T. Butts
openalex   +1 more source

Encoding Cumulation to Learn Perturbative Nonlinear Oscillatory Dynamics

Advanced Science, EarlyView.
Weak nonlinearities critically shape the long term behavior of oscillatory systems but are difficult to identify from data. A data‐driven framework is introduced to infer governing equations of weakly nonlinear oscillators from sparse and noisy observations.
Teng Ma, Ting‐Ting Gao, Wei Cui, Attilio Frangi, Gang Yan, Lin Zhao   +5 more
wiley   +1 more source