Computational prediction of drug–target interactions (DTIs) has become an essential task in
the drug discovery process. It narrows down the search space for interactions by suggesting …

Abstract Graph Convolutional Networks~(GCNs) are state-of-the-art graph based
representation learning models by iteratively stacking multiple layers of convolution …

Graphs are data structures that effectively represent relational data in the real world. Graph
representation learning is a significant task since it could facilitate various downstream …

Next Point-of-Interest (POI) recommendation which is of great value to both users and POI
holders is a challenging task since complex sequential patterns and rich contexts are …

“Nature shows us only the tail of the lion. But I do not doubt that the lion belongs to it even
though he cannot at once reveal himself because of his enormous size.”–Albert Einstein The …

We present recursive recurrent neural networks with attention modeling (R2AM) for lexicon-
free optical character recognition in natural scene images. The primary advantages of the …

Among different hybrid recommendation techniques, network-based entity recommendation
methods, which utilize user or item relationship information, are beginning to attract …

Matrix-factorization (MF)-based approaches prove to be highly accurate and scalable in
addressing collaborative filtering (CF) problems. During the MF process, the non-negativity …

With the emergence of online social networks, the social network-based recommendation
approach is popularly used. The major benefit of this approach is the ability of dealing with …

Experimental determination of drug-target interactions is expensive and time-consuming.
Therefore, there is a continuous demand for more accurate predictions of interactions using …