Fig. 1 | Nature Communications

Fig. 1

From: A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information

Fig. 1

The flowchart of the DTINet pipeline. DTINet first integrates a variety of drug-related information sources to construct a heterogeneous network and applies a compact feature learning algorithm to obtain a low-dimensional vector representation of the features describing the topological properties for each node. With the learned compact features X and Y for drugs and proteins (i.e., each row in X and Y represents the feature vector of a drug and a protein, respectively), DTINet then finds the best projection from drug space onto protein space, such that the projected feature vectors of drugs are geometrically close to the feature vectors of their known interacting proteins. The projection matrix Z is learned to minimize the difference between the known interaction matrix P and XZY T (see Supplementary Note 1 for more details). After that, DTINet infers new interactions for a drug by sorting its target candidates based on their geometric proximity to the projected feature vector of this drug in the projected space. The predicted new drug–target interactions can be further analyzed and experimentally validated

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