Partitioning gene expression data by data-driven Markov chain Monte Carlo

2015 ◽  
Vol 43 (6) ◽  
pp. 1155-1173 ◽  
Author(s):  
E.F. Saraiva ◽  
A.K. Suzuki ◽  
F. Louzada ◽  
L.A. Milan
Keyword(s):  
Gene Expression ◽  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Gene Expression Data ◽  
Data Driven ◽  
Expression Data

Double hit viral parasitism, polymicrobial CNS residency and perturbed proteostasis in Alzheimer’s disease: A data driven, in silico analysis of gene expression data

2020 ◽  
Vol 127 ◽  
pp. 124-135
Author(s):  
George D. Vavougios ◽  
Christiane Nday ◽  
Sygliti-Henrietta Pelidou ◽  
Sotirios G. Zarogiannis ◽  
Konstantinos I. Gourgoulianis ◽  
...  
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Gene Expression Data ◽  
In Silico ◽  
In Silico Analysis ◽  
Data Driven ◽  
Expression Data ◽  
Double Hit ◽  
Silico Analysis

scholarly journals Data-Driven Asthma Endotypes Defined from Blood Biomarker and Gene Expression Data

PLoS ONE ◽  
2015 ◽  
Vol 10 (2) ◽  
pp. e0117445 ◽  
Author(s):  
Barbara Jane George ◽  
David M. Reif ◽  
Jane E. Gallagher ◽  
ClarLynda R. Williams-DeVane ◽  
Brooke L. Heidenfelder ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Data Driven ◽  
Expression Data ◽  
Blood Biomarker

scholarly journals A data-driven method to learn a jump diffusion process from aggregate biological gene expression data

2022 ◽  
Vol 532 ◽  
pp. 110923
Author(s):  
Jia-Xing Gao ◽  
Zhen-Yi Wang ◽  
Michael Q. Zhang ◽  
Min-Ping Qian ◽  
Da-Quan Jiang
Keyword(s):  
Gene Expression ◽  
Diffusion Process ◽  
Gene Expression Data ◽  
Jump Diffusion ◽  
Data Driven ◽  
Expression Data ◽  
Jump Diffusion Process

Issues in Bayesian Analysis of Neural Network Models

1998 ◽  
Vol 10 (3) ◽  
pp. 749-770 ◽  
Author(s):  
Peter Müller ◽  
David Rios Insua
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Bayesian Analysis ◽  
Network Models ◽  
Feedforward Neural Networks ◽  
Data Driven ◽  
Neural Network Models

Stemming from work by Buntine and Weigend (1991) and MacKay (1992), there is a growing interest in Bayesian analysis of neural network models. Although conceptually simple, this problem is computationally involved. We suggest a very efficient Markov chain Monte Carlo scheme for inference and prediction with fixed-architecture feedforward neural networks. The scheme is then extended to the variable architecture case, providing a data-driven procedure to identify sensible architectures.

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