Gene expression data classification using topology and machine learning models

BMC Bioinformatics

Table 1 Neural network result

Data Name	Human dengue (#:4415 )		Human bone marrow (#: 469)		Human bowel disease (#: 1745)
Method	\(TP({\mathcal {Z}})\)	Full	\(TP({\mathcal {Z}})\)	Full	\(TP({\mathcal {Z}})\)	Full
# genes	1937	60619	5464	17258	1801	54715
Tr-Loss\((e^{-2})\)	5.95	10.06	05.16	05.70	13.11	9.58
Tr-Acc	97.84	96.64	99.72	99.15	96.63	97.56
Tr-F1	97.86	96.48	99.72	99.15	96.60	97.55
Tr-Prec	97.86	96.48	99.72	99.15	96.60	97.55
Tr-Rec	97.86	96.48	99.72	99.15	96.60	97.55
Ts-Loss \((e^{-2})\)	21.99	14.55	06.34	51.30	84.29	83.73
Ts-Acc	93.21	91.65	97.46	95.76	90.10	89.62
Ts-F1	92.26	90.67	96.95	95.74	90.34	89.66
Ts-Prec	93.48	90.67	96.95	95.74	90.34	89.66
Ts-Rec	93.48	90.67	96.95	95.74	90.34	89.66

The column \(TP({\mathcal {Z}})\) indicates the results on reduced gene set using topology. Full indicates results on the full gene set. Tr-Loss, Tr-Acc, Tr-F1, Tr-Prec,Tr-Rec is loss, accuracy, F1-score, precision, and recall on the training data. Whereas the prefix Ts- indicate the same on the test set

ISSN: 1471-2105