PharmKG: a dedicated knowledge graph benchmark for bomedical data mining

Abstract

Biomedical knowledge graphs (KGs), which can help with the understanding of complex biological systems and pathologies, have begun to play a critical role in medical practice and research. However, challenges remain in their embedding and use due to their complex nature and the specific demands of their construction. Existing studies often suffer from problems such as sparse and noisy datasets, insufficient modeling methods and non-uniform evaluation metrics. In this work, we established a comprehensive KG system for the biomedical field in an attempt to bridge the gap. Here, we introduced PharmKG, a multi-relational, attributed biomedical KG, composed of more than 500 000 individual interconnections between genes, drugs and diseases, with 29 relation types over a vocabulary of ~8000 disambiguated entities. Each entity in PharmKG is attached with heterogeneous, domain-specific information obtained from multi-omics data, i.e. gene expression, chemical structure and disease word embedding, while preserving the semantic and biomedical features. For baselines, we offered nine state-of-the-art KG embedding (KGE) approaches and a new biological, intuitive, graph neural network-based KGE method that uses a combination of both global network structure and heterogeneous domain features. Based on the proposed benchmark, we conducted extensive experiments to assess these KGE models using multiple evaluation metrics. Finally, we discussed our observations across various downstream biological tasks and provide insights and guidelines for how to use a KG in biomedicine. We hope that the unprecedented quality and diversity of PharmKG will lead to advances in biomedical KG construction, embedding and application.

Explainable Biomedical Recommendations via Reinforcement Learning Reasoning on Knowledge Graphs

“…Biomedical KGs are becoming increasingly popular within industry and as open-source initiatives. Examples include, BioKG (Walsh et al, 2020), DRKG (Ioannidis et al, 2020), Clinical KG (Santos et al, 2020), Hetionet (Himmelstein et al, 2017, OpenBi-oLink (Breit et al, 2020), OGBL-BIOKG and PharmKG (Zheng et al, 2020b).…”

Predicting Potential Drug Targets Using Tensor Factorisation and Knowledge Graph Embeddings

“…For instance, in a biomedical knowledge graph, various biological entities are modelled as nodes, whilst millions of complex biomedical relationships are modelled as edges (links) connecting nodes. In recent years, many researchers and pharmaceutical companies have been building biomedical knowledge graphs to address various drug discovery challenges, e.g., Hetionet [13], PharmKG [37] and Rosalind's knowledge graph [21].…”

Medical Knowledge Graph in Chinese Using Deep Semantic Mobile Computation Based on IoT and WoT

“…In recent years, as the maturity of technology and more emphasis on healthcare, it has become more and more popular to apply knowledge graph in the medical field and has attracted much attention from researchers in computer and medical to combine these two fields [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26]. Rotmensch et al [11] in 2017 proposed learning a health knowledge graph from electronic medical records by using probabilistic.…”

“…Chen et al [16] in 2020 proposed robustly extracting medical knowledge from EHRs, in which nonlinear functions are adopted in building the causal graph to better understand exiting model assumptions. Zheng et al [17] put forward a dedicated knowledge graph benchmark for biomedical data mining in 2020. Also, medical knowledge graph can be applied in specific departments, for example, Xie et al [24] in 2018 presented a data-driven traditional Chinese medicine knowledge discovery method based on a knowledge graph.…”