A multiple kernel learning algorithm for drug-target interaction prediction

Globalized Bipartite Local Learning Model for Drug-Target Interaction Prediction

Computational methods provide efficient ways to predict possible interactions between drugs and targets, which is critical in drug discovery. Supervised prediction with bipartite Local Model recently has been shown to be effective for prediction of drug-target interactions. However, this pure “local” model is unapplicable to new drug or target candidates that currently have no known interaction...

Similarity based learning method for drug target interaction prediction

Online Multiple Kernel Learning for Structured Prediction

Despite the recent progress towards efficient multiple kernel learning (MKL), the structured output case remains an open research front. Current approaches involve repeatedly solving a batch learning problem, which makes them inadequate for large scale scenarios. We propose a new family of online proximal algorithms for MKL (as well as for group-LASSO and variants thereof), which overcomes that...

Drug-target interaction prediction using ensemble learning and dimensionality reduction.

Experimental prediction of drug-target interactions is expensive, time-consuming and tedious. Fortunately, computational methods help narrow down the search space for interaction candidates to be further examined via wet-lab techniques. Nowadays, the number of attributes/features for drugs and targets, as well as the amount of their interactions, are increasing, making these computational metho...

Deep Learning for Drug Target Prediction

An important computational tool in drug design is target prediction where either for a given chemical structure the interacting biomolecules (e.g. proteins) must be identified. Chemical structures interact with different biomolecules if they have similar 3D structure. Thus, the outputs of the prediction are highly interdependent from each other. Furthermore, we have partially labelled molecules...