BLOGS

NeuralKG on DrugBank

Author: NeuralKG Core Team   Date: 2022.03.21

In DrugBank, conditions are often specific medical states, including diseases, symptoms, and other health-related characteristics or problems. Conditions may also be used to describe other clinical human phenomena, such as procedures, therapies, and the presence or absence of certain genes. [more]

Drug Repurposing Knowledge Graph on NeuralKG

Author: NeuralKG Core Team   Date: 2022.03.21

Drug Repurposing Knowledge Graph (DRKG) is a comprehensive biological knowledge graph relating genes, compounds, diseases, biological processes, side effects and symptoms. DRKG includes information from six existing databases including DrugBank, Hetionet, GNBR, String, IntAct and DGIdb, and data collected from recent publications particularly related to Covid19. It includes 97,238 entities belonging to 13 entity-types; and 5,874,261 triplets belonging to 107 edge-types.[more]

Gene Ontology with NeuralKG

Author: NeuralKG Core Team   Date: 2022.03.21

The Gene Ontology(GO) resource provides a computational representation of our current scientific knowledge about the functions of genes (or, more properly, the protein and non-coding RNA molecules produced by genes) from many different organisms, from humans to bacteria. It is widely used to support scientific research, and has been cited in tens of thousands of publications. [more]

NeuralKG for Recommendation

Author: NeuralKG Core Team   Date: 2022.03.17

It is worth mentioning that NeuralKG’s use is not limited to the standard benchmarks such as FB15K237, WN18RR. We could construct self-defined Knowledge Graphs datasets and get strong baseline for more comprehensive experiments with NeuralKG. We could use an example to illustrate how to apply NeuralKG on self-defined datasets. [more]

Brief Introduction to NeuralKG

Author: NeuralKG Core Team   Date: 2022.03.01

NeuralKG is an open-source Python-based library for diverse representation learning of knowledge graphs. It implements three different series of Knowledge Graph Embedding (KGE) methods, including conventional KGEs, GNN-based KGEs, and Rule-based KGEs. With a unified framework, NeuralKG successfully reproduces link prediction results of these methods on benchmarks, freeing users from the laborious task of reimplementing them… [more]