Data rarely exists in isolation, most of it is connected, whether through people, systems, or events. Think about your social networks, the complex pathways within our bodies, or even the vast web of ...

Adverse drug reactions (ADRs) remain a major barrier to safe therapeutic developments. A key challenge is our limited understanding of their underlying mechanisms. In this study, we investigated ...

This guide shows how to compute graph embeddings in Neo4j using the Graph Data Science (GDS) library and use them in downstream ML tasks. We cover the Node2Vec, Fast Random Projection (FastRP), and ...

Recent advances in machine learning have opened new productive research directions in the study of networks (or graphs). Graph embeddings are paradigmatic examples. They represent the structure of a ...

Here we provide the codes, some of the processed data, and important results of the DualNetGO paper. DualNetGO is comprised of two components: a graph encoder for extracting graph information or ...

As the world becomes increasingly data-driven, the demand for accurate and efficient search technologies has never been higher. Traditional search engines, while powerful, often struggle to meet the ...

$ git clone https://github.com/krishnanlab/pecanpy.git $ cd pecanpy $ pip install -e . where -e means "editable" mode so you don't have to reinstall every time you ...

Pseudogenes are indicating more and more functional potentials recently, though historically were regarded as relics of evolution. Computational methods for predicting pseudogene functions on Gene ...

Recently, graph embedding techniques have been widely used in the analysis of various networks, but most of the existing embedding methods omit the network dynamics and the multiplicity of edges, so ...