Genome-wide prediction and prioritization of human aging genes by data fusion: a machine learning approach

BMC Genomics

Table 12 Comparison of the evaluation metric across data sources

Data source	Recall	Specificity	Precision	Accuracy	F_Measure
Literature	0.58098	0.61453	0.5888	0.5981	0.58478
Annotation	0.77685	0.78668	0.76645	0.78165	0.77133
Pathways	0.73268	0.74538	0.7204	0.73893	0.72605
Gene Ontology	0.79303	0.78843	0.76315	0.78958	0.77703
Phenotype	0.7946	0.81968	0.8158	0.80695	0.80488
Intrinsic properties	0.67963	0.77035	0.78945	0.71835	0.72965
Sequence	0.6901	0.72828	0.71713	0.70885	0.70305
Interaction	0.7378	0.7724	0.76645	0.7543	0.75135
Gene expression	0.75635	0.82148	0.82235	0.7864	0.78735
Regulatory	0.77355	0.79203	0.77633	0.78163	0.77393

ISSN: 1471-2164