Drug discovery is adapting to novel technologies such as data science, informatics, and
artificial intelligence (AI) to accelerate effective treatment development while reducing costs …

Link prediction finds missing links (in static networks) or predicts the likelihood of future links
(in dynamic networks). The latter definition is useful in network evolution (Wang et al., 2011; …

Circular RNAs (circRNAs) are a class of single-stranded, covalently closed RNA molecules
with a variety of biological functions. Studies have shown that circRNAs are involved in a …

In this paper, we propose a novel representation learning framework, namely HIN2Vec, for
heterogeneous information networks (HINs). The core of the proposed framework is a neural …

“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 …

Most real systems consist of a large number of interacting, multi-typed components, while
most contemporary researches model them as homogeneous information networks, without …

The ability to computationally predict whether a compound treats a disease would improve
the economy and success rate of drug approval. This study describes Project Rephetio to …

Graph-structured data arise naturally in many different application domains. By representing
data as graphs, we can capture entities (ie, nodes) as well as their relationships (ie, edges) …

In the fast-growing scholarly big data background, social network technologies have recently
aroused widespread attention in academia and industry. The concept of academic social …

In social networks, link prediction predicts missing links in current networks and new or
dissolution links in future networks, is important for mining and analyzing the evolution of …