A semi-supervised machine learning framework for microRNA classification

Table 1 MVCT performance results for all six data sets over 11 iterations of learning. Results demonstrate average area under the precision-recall curves. Standard deviations were in the range of 0.001 to 0.003 for all experiments and are omitted from the table for clarity

Iteration	hsa exp-based	hsa seq-based	mmu exp-based	mmu seq-based	dme exp- based	dme seq-based	bta exp-based	bta seq-based	gga exp-based	gga seq- based	eca exp-based	eca seq-based
–	0.596	0.344	0.714	0.822	0.810	0.864	0.778	0.357	0.925	0.893	0.921	0.875
1	0.681	0.448	0.795	0.881	0.854	0.920	0.866	0.478	0.932	0.909	0.918	0.881
2	0.705	0.568	0.797	0.906	0.884	0.920	0.860	0.566	0.930	0.905	0.926	0.893
3	0.721	0.678	0.813	0.903	0.893	0.920	0.865	0.585	0.927	0.909	0.934	0.886
4	0.752	0.735	0.872	0.912	0.893	0.919	0.850	0.778	0.920	0.912	0.939	0.941
5	0.748	0.734	0.879	0.911	0.886	0.925	0.863	0.726	0.931	0.917	0.952	0.946
6	0.781	0.739	0.921	0.920	0.883	0.912	0.849	0.783	0.923	0.915	0.947	0.947
7	0.771	0.747	0.917	0.910	0.887	0.922	0.871	0.773	0.930	0.911	0.954	0.952
8	0.791	0.744	0.937	0.912	0.882	0.920	0.855	0.734	0.951	0.916	0.943	0.949
9	0.772	0.738	0.928	0.911	0.920	0.932	0.860	0.744	0.957	0.918	0.956	0.955
10	0.773	0.761	0.941	0.908	0.903	0.923	0.865	0.765	0.961	0.917	0.952	0.961
11	0.779	0.761	0.955	0.912	0.901	0.921	0.865	0.809	0.964	0.927	0.959	0.961

ISSN: 1479-7364