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DNA-methylation datasets of 13 different cell types or tissues were used: 5 datasets were implemented as a training-set for identification of the Epigenetic-Aging-Signature and 8 datasets were reserved for subsequent validation (table 1).
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Contrastive evaluations in benchmarking datasets are implemented with several state-of-the-art CNN-based crowd counting approaches.
Bootstrapping of 100 datasets was implemented.
A phylogeny of the concatenated dataset was implemented in MrBayes.
The complete and pairwise deletion of gaps or missing data were implemented with the datasets containing 49,751 and 104,097 positions, respectively.
Machine learning algorithms, namely Multiple Linear Regression, Artificial Neural Network, REPTree and Random Tree classifiers were implemented on the dataset to predict the risk level of applicants.
Three models, which varied only in the parameter α, were implemented for each dataset.
Models of molecular evolution which had been selected using the Akaike Information Criterion (AIC) implemented in jModeltest [ 82] or MrModeltest 2.3 [ 83] were implemented on this dataset.
To investigate the performance of algorithms, 5692 PDB structures registered in EzCatDB were implemented for the PDB datasets.
Perl and R scripts were implemented to integrate the datasets, annotate addiction vulnerable SNPs and perform statistical tests.
These analyses were implemented on the combined dataset.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com