Results 41 to 50 of about 5,004,642 (164)

Finite Mixtures of Semiparametric Bayesian Survival Kernel Machine Regressions: Application to Breast Cancer Gene Pathway Subgroup Analysis

Journal of the Royal Statistical Society Series C: Applied Statistics, 2021
Abstract A gene pathway is defined as a set of genes that functionally work together to regulate a certain biological process. Gene pathway expression data, which is a special case of highly correlated high-dimensional data, exhibits the ‘small n and large p’ problem. Pathway analysis can take into account the dependency structures among
Zhang, Lin, Kim, Inyoung
openaire   +1 more source

Hepatotoxicity assessment investigations on PFASs targeting L-FABP using binding affinity data and machine learning-based QSAR model

Ecotoxicology and Environmental Safety, 2023
Per- and polyfluoroalkyl substances (PFASs) are persistent organic pollutants that have been detected in various environmental media and human serum, but their safety assessment remains challenging.
Jiayi Zhao, Xiaoyue Shi, Zhiqin Wang, Sijie Xiong, Yongfeng Lin, Xiaoran Wei, Yanwei Li, Xiaowen Tang   +7 more
doaj   +1 more source

Predicting Flatfish Growth in Aquaculture Using Bayesian Deep Kernel Machines

Applied Sciences
Olive flounder (Paralichthys olivaceus) is a key aquaculture species in South Korea, but its production has been challenged by rising mortality under environmental stress from key environmental factors such as water temperature, dissolved oxygen, and ...
Junhee Kim, Seung-Won Seo, Ho-Jin Jung, Hyun-Seok Jang, Han-Kyu Lim, Seongil Jo   +5 more
doaj   +1 more source

Machine Learning Method in Study of Nuclear Charge Radius

Yuanzineng kexue jishu, 2023
The charge radius of an atomic nucleus describes its charge distribution, which is important for the understanding of the nucleon-nucleon interaction in medium.
DONG Xiaoxu;GENG Lisheng
doaj   +1 more source

The association between exposure to volatile organic compounds and serum lipids in the US adult population

Lipids in Health and Disease, 2023
Background and Aim Epidemiological evidence on the relationship between exposure to volatile organic compounds (VOCs), both single and mixed, and serum lipid levels is limited, and their relationship remains unclear.
Wen-Yu Chen, Yan-Peng Fu, Hui Tu, Wen Zhong, Liang Zhou   +4 more
doaj   +1 more source

Bayesian Wavelet and Fourier Transform Kernel Regression and Classification in RKHS [PDF]

, 2021
Kernel methods are often used for nonlinear regression and classification in machine learning because they are computationally cheap and accurate. Fourier basis and wavelet basis are the bases that can efficiently approximate the kernel functions.
Zhang, Xueying
core   +1 more source

Bayesian kernel machine regression for count data: modelling the association between social vulnerability and COVID-19 deaths in South Carolina. [PDF]

J R Stat Soc Ser C Appl Stat, 2023
Abstract The COVID-19 pandemic created an unprecedented global health crisis. Recent studies suggest that socially vulnerable communities were disproportionately impacted, although findings are mixed. To quantify social vulnerability in the US, many studies rely on the Social Vulnerability Index (SVI), a county-level measure comprising ...
Mutiso F, Li H, Pearce JL, Benjamin-Neelon SE, Mueller NT, Neelon B.   +5 more
europepmc   +3 more sources

kernlab - An S4 package for kernel methods in R [PDF]

, 2004
kernlab is an extensible package for kernel-based machine learning methods in R. It takes advantage of R's new S4 object model and provides a framework for creating and using kernel-based algorithms. The package contains dot product primitives (kernels),
Zeileis, Achim, Kurt Hornik, Smola, Alex, Alexandros Smola, Achim Zeileis, Smola, Alexandros, Karatzoglou, Alexandros, Hornik, Kurt, Alexandros Karatzoglou, Technische Universität Wien, Alex Smola   +10 more
core   +1 more source

Efficient hyperparameter tuning for kernel ridge regression with Bayesian optimization [PDF]

, 2022
Machine learning methods usually depend on internal parameters-so called hyperparameters-that need to be optimized for best performance. Such optimization poses a burden on machine learning practitioners, requiring expert knowledge, intuition or ...
Rinke Patrick, Todorovic Milica, Stuke Annika   +2 more
core   +1 more source

Comparative efficacy of three Bayesian variable selection methods in the context of weight loss in obese women

Frontiers in Nutrition, 2023
The use of high-dimensional data has expanded in many fields, including in clinical research, thus making variable selection methods increasingly important compared to traditional statistical approaches.
Nicola Pesenti, Piero Quatto, Elena Colicino, Raffaella Cancello, Massimo Scacchi, Massimo Scacchi, Antonella Zambon, Antonella Zambon   +7 more
doaj   +1 more source