The AUC and AUPR values of the five methods for the four types of proteins in each validation set (previous and updated dataset).

<p>The AUC and AUPR values of the five methods for the four types of proteins in each validation set (previous and updated dataset).</p

An example of SELF-BLM predicting the targets of a drug.

<p>In the previous dataset, it was known that proteins (HTR2A and HTR2C) bind to a drug (Olanzapine), but it was not known that other proteins (HTR1B, HTR1D, and HTR1F) also bind to the drug. Thus, in BLM, HTR2A and HTR2C are labeled as positive, and HTR1B, HTR1D and HTR1F are labeled as negative. Because the protein (HTR1E) is more similar to negatively labeled proteins than to positively labeled proteins, the protein is predicted to be negative. However, in SELF-BLM, these proteins (HTR1B, HTR1D, and HTR1F) are unlabeled. Therefore, the protein (HTR1E) is predicted as positive. There was no information suggesting that the protein (HTR1E) binds to the drug (Olanzapine) in the previous data, but it was later revealed that the protein indeed binds to the drug.</p

Rankings of AUPR trends by the different methods according to the updated dataset.

<p>In each panel, y-axis shows the rank representation of the AUPR value. A) the ranking in type of enzymes, B) the ranking in type of ion channels, C) the ranking in type of GPCRs, D) the ranking in type of nuclear receptor.</p

The potential AUPRs of the five methods for the four types of proteins.

<p>The potential AUPRs of the five methods for the four types of proteins.</p

The number of potential interactions found by each method.

<p>X-axis represents the accumulated percentage of positively predicted interactions in each method, y-axis represents the number of correctly predicted potential interactions. A) The number of potential interactions according to type of enzyme. B) The number of potential interactions according to type of ion channel. C) The number of potential interactions according to type of GPCR. D) The number of potential interactions according to type of nuclear receptor.</p

The number of drugs, target proteins, interactions and updated interactions of each type.

<p>The number of drugs, target proteins, interactions and updated interactions of each type.</p

Overview of the proposed method.

<p><b>(A)</b> From known information, drug-target interactions are classified into positive and unknown interactions (matrix A). Using similarity scores of drugs (matrix <i>S</i>^<i>d</i>) and targets (matrix <i>S</i>^<i>t</i>), we performed k-medoids clustering. If any of the drugs in a cluster do not interact with the cluster of the target protein, we considered the drugs in the cluster as having a negative interaction with the protein. Finally, drug-target interactions are classified into positive, negative and unknown interactions (matrix <i>A</i>_<i>n</i>). Yellow rectangle: target protein, blue circle: drugs having positive interactions with the target protein, red circle: drugs having negative interactions with the target protein, gray circle: drugs having unknown interactions with the target protein. <b>(B)</b> A self-training SVM repeatedly trains the unlabeled data (unknown) as positive or negative. Finally, local classification models that can find potential interactions are constructed.</p