BMC Bioinformatics

Table 2 This table shows the main results comparing the feature selection benchmarks of our approach with Lasso and ℓ ₁-SVM on the spiked data-set. Given results correspond to the average results over 10 repetitions of the classifier with 6 non-zero values

From: Sparse Proteomics Analysis – a compressed sensing-based approach for feature selection and classification of high-dimensional proteomics mass spectrometry data

	SPA				Lasso				ℓ ₁-SVM
Concentration	TP ^[a]	Sens ^[b]	Specs ^[c]	B. Acc ^[d]	TP	Sens	Spec	B. Acc	TP	Sens	Spec	B. Acc
0.075pMol/L	2	0.333	1.000	0.667	1	0.167	1.000	0.583	1	0.167	1.000	0.583
3.03pMol/L	4	0.667	1.000	0.833	2	0.333	1.000	0.667	1	0.167	1.000	0.583
0.30nMol/L	2	0.333	1.000	0.667	1	0.167	1.000	0.583	1	0.167	1.000	0.583
0.76nMol/L	2	0.333	1.000	0.667	2	0.333	1.000	0.667	2	0.333	1.000	0.667
121.21nMol/L	3	0.500	1.000	0.750	2	0.333	1.000	0.667	2	0.333	1.000	0.667

^[a]TP: Number of spiked peaks that are correctly detected
^[b]Sens: Sensitivity in detecting spiked peaks (T P/(T P+F N))
^[c]Spec: Specificity in detecting spiked peaks (T N/(F P+T N))
^[d]B. Acc: Balanced Accuracy (\( \frac {\text {sens.} + \text {spec.}}{2}\))

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ISSN: 1471-2105

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