• BLM-NII: On one hand, excluding target ti, make a list of all other known targets of ligand lj, as well as a separate list of the targets not known to be targeted ligand lj. The known targets were given a label +1 and the others a label −1. Then, look for a classification rule that tried to discriminate the +1-labeled data from the −1-labeled data using the available genomic sequence data for the targets. This rule was applied to predict the label of target ti and ligand lj. On the other hand, fixing the same target ti and excluding ligand lj, make a list of all other known ligands targeting ti, as well as a list of ligands not known to target ti. Similar with before, ligands known to target ti were given the label +1 and the others were given the label −1. We looked for a classification rule that tried to discriminate the +1-labeled data from the −1-labeled data, using the available chemical structure data for the ligands. This rule was also used to predict the label of target ti and ligand lj. At last, the two results were combined to generate a final label. For new targets or ligands, a neighbor-based interaction-profile inferring was applied to get an interaction profile.