Prediction of Cancer Proteins by Integrating Protein Interaction, Domain Frequency, and Domain Interaction Data Using Machine Learning Algorithms

Table 1

Performance comparison for the individual algorithm sorted by 1 value.

Type	Algorithm	ACC	SPE	SEN	1	MCC	PPV	AUC
Trees	LMT	0.772	0.802	0.748	0.774	0.548	0.821	0.774
Trees	SimpleCart	0.770	0.804	0.742	0.773	0.546	0.825	0.775
Trees	J48	0.767	0.799	0.741	0.770	0.538	0.818	0.771
Trees	J48graft	0.767	0.799	0.742	0.769	0.538	0.818	0.771
Trees	REPTree	0.766	0.796	0.741	0.767	0.536	0.818	0.767
Trees	FT	0.763	0.798	0.735	0.766	0.528	0.821	0.766
Rules	DTNB	0.760	0.804	0.728	0.763	0.527	0.833	0.765
Trees	NBTree	0.760	0.796	0.733	0.763	0.527	0.819	0.764
Trees	RandomForest	0.760	0.777	0.744	0.761	0.524	0.791	0.761
Rules	Ridor	0.718	0.910	0.650	0.758	0.492	0.954	0.780
Rules	Jrip	0.754	0.790	0.726	0.757	0.512	0.817	0.757
Rules	DecisionTable	0.752	0.790	0.722	0.756	0.509	0.817	0.757
Rules	PART	0.744	0.758	0.745	0.748	0.494	0.754	0.751
Lazy	Kstar	0.744	0.766	0.725	0.744	0.491	0.784	0.744
Functions	MultilayerPerceptron	0.724	0.792	0.683	0.734	0.462	0.835	0.739
Trees	LADTree	0.720	0.762	0.696	0.727	0.447	0.788	0.729
Trees	RandomTree	0.721	0.721	0.720	0.721	0.440	0.723	0.721
Lazy	LWL	0.610	1.000	0.560	0.720	0.350	1.000	0.780
Misc	VFI	0.610	1.000	0.560	0.720	0.350	1.000	0.780
Rules	ConjunctiveRule	0.610	1.000	0.560	0.720	0.350	1.000	0.780
Trees	DecisionStump	0.610	1.000	0.560	0.720	0.350	1.000	0.780
Trees	ADTree	0.709	0.762	0.685	0.719	0.433	0.787	0.724
Bayes	BayesNet	0.714	0.755	0.683	0.717	0.431	0.796	0.719
Lazy	IB1	0.713	0.716	0.711	0.713	0.427	0.718	0.713
Lazy	Ibk	0.713	0.716	0.711	0.713	0.427	0.718	0.713
Functions	Logistic	0.669	0.652	0.692	0.672	0.342	0.606	0.673
Bayes	BayesianLogisticRegression	0.670	0.655	0.687	0.671	0.342	0.622	0.673
Functions	SimpleLogistic	0.669	0.652	0.691	0.670	0.342	0.605	0.672
Functions	SMO	0.665	0.641	0.699	0.667	0.334	0.580	0.670
Functions	VotedPerceptron	0.642	0.606	0.721	0.657	0.305	0.467	0.664
Rules	Nnge	0.522	0.511	0.858	0.641	0.128	0.052	0.686
Rules	OneR	0.635	0.629	0.641	0.635	0.269	0.610	0.634
Functions	RBFNetwork	0.624	0.603	0.655	0.627	0.254	0.529	0.629
Bayes	NaiveBayes	0.598	0.571	0.660	0.614	0.214	0.410	0.615
Bayes	NaiveBayesUpdateable	0.598	0.571	0.660	0.614	0.214	0.410	0.615
Bayes	NaiveBayesSimple	0.598	0.571	0.660	0.613	0.214	0.411	0.614
Bayes	NaiveBayesMultinomial	0.576	0.554	0.634	0.590	0.168	0.366	0.594
Misc	HyperPipes	0.570	0.568	0.579	0.571	0.143	0.534	0.572
Rules	ZeroR	0.510	0.510	0.510	0.510	0.010	0.510	0.510