BMC Bioinformatics

Table 6 Performance scores for models constructed on the Dunedin data and tested on validation data sets

From: A comparison of feature selection methodologies and learning algorithms in the development of a DNA methylation-based telomere length estimator

Estimator	Feature- selection method	Features in estimator (CpGs)
			EXTEND			TWIN
			MAE	MAPE	Correlation [CI 83.4%]	MAE	MAPE	Correlation [CI 83.4%]
Baseline	None	832	0.550	38.31	0.166	0.711	114.50	0.070
PCA-EN TL	PCA	111*	0.570	39.37	0.295 [0.201, 0.384]	0.718	112.46	0.074 [− 0.031, 0.177]
F-test-0.01-EN TL	F-test (0.01)	3893	0.614	42.93	− 0.003 [− 0.103, 0.097]	0.728	106.52	0.119 [0.015, 0.221]
F-test-0.05-EN TL	F-test (0.05)	5448	0.626	43.35	0.07 [− 0.031, 0.169]	0.752	102.35	0.092 [− 0.012, 0.194]
r-EN TL	Pearson’s R	251	0.599	41.31	0.136 [0.036, 0.233]	0.718	116.93	− 0.102 [− 0.204, 0.002]
Boost-EN TL	Gradient Boosting	446	0.601	41.39	0.085 [− 0.016, 0.184]	0.725	114.49	0.052 [− 0.053, 0.155]
MI-EN TL	Mutual Information	407	0.640	43.51	0.203 [0.105, 0.297]	0.753	107.83	− 0.067 [− 0.17, 0.038]
LSVR-EN TL	Linear SVR	4945	0.620	43.13	0.114 [0.014, 0.212]	0.760	108.37	0.006 [− 0.098, 0.11]
RF-EN TL	Random Forest	1059	0.615	41.83	0.135 [0.035, 0.232]	0.762	106.23	0.044 [− 0.061, 0.148]

Metrics include MAE, MAPE and Pearson correlation (predicted and actual TL). Confidence intervals are shown for correlations. The number of features in each estimator is also shown. The estimator Baseline refers to the model that utilised elastic net regression with no prior feature-selection stage. Estimator names denote the feature-selection method and the regression algorithm used e.g., F-test-0.01-EN TL refers to the F-test feature-selection stage with FDR of 0.01, followed by application of elastic net. *denotes principal components. Values in bold text relate to a selection of the best models

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ISSN: 1471-2105

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