Predicting drug-disease associations by using similarity constrained matrix factorization

BMC Bioinformatics

Table 1 The summary of SCMFDD-S dataset and SCMFDD-L dataset

Dataset	Drugs	Diseases	Associations	Richness	Drug features
Dataset	Drugs	Diseases	Associations	Richness	Substructure	Target	Enzyme	Pathway	Drug Interactions
SCMFDD-S	269	598	18,416	11.4%	881	623	247	465	2086
SCMFDD-L	1323	2834	49,217	1.31%	881	N.A.	N.A.	N.A.	N.A.

Numbers for drug features represent the numbers of descriptors. For example, the PubChem Compound defines 881 types of substructure descriptors for compound substructures, and a drug has some substructures and is thus described by a subset of substructure descriptors. Richness is the ratio of association number vs drug-disease pair number. N.A. indicates that the information is not available

ISSN: 1471-2105