Concordance-based batch effect correction for large-scale metabolomics
For a large-scale metabolomics study, sample collection, preparation, and analysis may last
several days, months, or even (intermittently) over years. This may lead to apparent batch …
several days, months, or even (intermittently) over years. This may lead to apparent batch …
A novel statistical modeling of air pollution and the COVID-19 pandemic mortality data by Poisson, geometric, and negative binomial regression models with fixed and …
Abstract The coronavirus disease 2019 (COVID-19) pandemic was defined by the World
Health Organization (WHO) as a global epidemic on March 11, 2020, as the infectious …
Health Organization (WHO) as a global epidemic on March 11, 2020, as the infectious …
Comparative study by adding bootstrapping stage in construction of biological networks
MA Kaygusuz, V Purutçuoğlu - Journal of Dynamics and Games, 2024 - aimsciences.org
Model selection methods are very popular in high-dimensional settings in recent years due
to the availability of massive amounts of data, specifically from genetical, image progressing …
to the availability of massive amounts of data, specifically from genetical, image progressing …
[PDF][PDF] Random forest regression and an alternative model selection procedure
MA Kaygusuz, V Purutçuoğlu - Proceedings of the 2nd …, 2021 - researchgate.net
Multiple testing procedures are very popular in recent years due to the huge amount of data
in genetics, engineering and finance. In order to construct proper models for those data …
in genetics, engineering and finance. In order to construct proper models for those data …
[PDF][PDF] Arti ficial neural networks for nonparametric regression with biological data
MA Kaygusuz, AN Somuncuoğlu, V Purutçuoğlu Gazi - 2021 - researchgate.net
In this paper, we consider the deep learning network model in the construction of biological
networks. This approach has been commonly used in recent years for modeling complex …
networks. This approach has been commonly used in recent years for modeling complex …
The Model Selection Methods for Sparse Biological Networks
MA Kaygusuz, V Purutçuoğlu - … of the International Conference on Artificial …, 2020 - Springer
It is still crucial problem to estimate high dimensional graphical models and to choose the
regularization parameter in dependent data. There are several classical methods such as …
regularization parameter in dependent data. There are several classical methods such as …
Novel model selection criteria on high dimensionalbiological networks
GB Bülbül - 2019 - open.metu.edu.tr
Gaussian graphical model (GGM) is an useful tool to describe the undirected associ-ations
among the genes in the sparse biological network. To infer such high dimen-sional …
among the genes in the sparse biological network. To infer such high dimen-sional …