Knowledge graphs and their applications in drug discovery

Maclean, Finlay

doi:10.1080/17460441.2021.1910673

Cited by 48 publications

(33 citation statements)

References 76 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other drug-repositioning methods utilize knowledge-driven approaches that make use of graph- or network-based data mining methods 13 , 28 that integrate data from genome-wide association studies (GWASs), gene expression, biological pathways, and molecular interactions to search for new indication opportunities for drugs. The biggest limitation of such annotation-driven methods is that the sparsity and biases of our knowledge of systems biology make it challenging to identify opportunities beyond the relatively obvious “low-hanging fruits.” This is because functional annotation of even well-researched genes cannot be considered as complete; experimental assays are limited in the type of information they can discover and, most notably, experimental designs are often guided by what is already known or expected.…”

Section: Drug Indication Extensionmentioning

confidence: 99%

Systematic indication extension for drugs using patient stratification insights generated by combinatorial analytics

Das¹,

Taylor²,

Beaulah³

et al. 2022

Patterns

View full text Add to dashboard Cite

Section: Drug Indication Extensionmentioning

confidence: 99%

Systematic indication extension for drugs using patient stratification insights generated by combinatorial analytics

Das¹,

Taylor²,

Beaulah³

et al. 2022

Patterns

View full text Add to dashboard Cite

“…Even though a variety of relation types (e.g., literature co-occurrence, associations, etc.) can be leveraged by network-topology algorithms for various applications, causal relations are particularly useful as they can be used to infer the effect of any given node on another by reasoning over the KG [ 7 ]. Nonetheless, not all interactions included in a given KG are necessarily biologically relevant as they may be context-specific, such as to a particular cell type, tissue or disease.…”

Section: Introductionmentioning

confidence: 99%

Causal reasoning over knowledge graphs leveraging drug-perturbed and disease-specific transcriptomic signatures for drug discovery

et al. 2022

View full text Add to dashboard Cite

Network-based approaches are becoming increasingly popular for drug discovery as they provide a systems-level overview of the mechanisms underlying disease pathophysiology. They have demonstrated significant early promise over other methods of biological data representation, such as in target discovery, side effect prediction and drug repurposing. In parallel, an explosion of -omics data for the deep characterization of biological systems routinely uncovers molecular signatures of disease for similar applications. Here, we present RPath, a novel algorithm that prioritizes drugs for a given disease by reasoning over causal paths in a knowledge graph (KG), guided by both drug-perturbed as well as disease-specific transcriptomic signatures. First, our approach identifies the causal paths that connect a drug to a particular disease. Next, it reasons over these paths to identify those that correlate with the transcriptional signatures observed in a drug-perturbation experiment, and anti-correlate to signatures observed in the disease of interest. The paths which match this signature profile are then proposed to represent the mechanism of action of the drug. We demonstrate how RPath consistently prioritizes clinically investigated drug-disease pairs on multiple datasets and KGs, achieving better performance over other similar methodologies. Furthermore, we present two case studies showing how one can deconvolute the predictions made by RPath as well as predict novel targets.

show abstract

“…Knowledge graphs are being used almost everywhere today [ 4 , 5 ]. An explanation using an example should be sufficient for people to understand this concept even better.…”

Section: Introductionmentioning

confidence: 99%

Application of Medical Knowledge Graphs in Cardiology and Cardiovascular Medicine: A Brief Literature Review

Wang

et al. 2022

Adv Ther

View full text Add to dashboard Cite

A knowledge graph is defined as a collection of interlinked descriptions of concepts, relationships, entities and events. Medical knowledge graphs have been the most recent advances in technology, therapy and medicine. Nowadays, a number of specific uses and applications rely on knowledge graphs. The application of the knowledge graph, another form of artificial intelligence (AI) in cardiology and cardiovascular medicine, is a new concept, and only a few studies have been carried out on this particular aspect. In this brief literature review, the use and importance of disease-specific knowledge graphs in exploring various aspects of Kawasaki disease were described. A vision of individualized knowledge graphs (iKGs) in cardiovascular medicine was also discussed. Such iKGs would be based on a modern informatics platform of exchange and inquiry that could comprehensively integrate biologic knowledge with medical histories and health outcomes of individual patients. This could transform how clinicians and scientists discover, communicate and apply new knowledge. In addition, we also described how a study based on the comprehensive longitudinal evaluation of dietary factors associated with acute myocardial infarction and fatal coronary heart disease used a knowledge graph to show the dietary factors associated with cardiovascular diseases in Nurses’ Health Study data. To conclude, in this fast-developing world, medical knowledge graphs have emerged as attractive methods of data storage and hypothesis generation. They could be a major and effective tool in cardiology and cardiovascular medicine and play an important role in reaching effective clinical decisions during treatment and management of patients in the cardiology department.

show abstract

Knowledge graphs and their applications in drug discovery

Cited by 48 publications

References 76 publications

Systematic indication extension for drugs using patient stratification insights generated by combinatorial analytics

Systematic indication extension for drugs using patient stratification insights generated by combinatorial analytics

Causal reasoning over knowledge graphs leveraging drug-perturbed and disease-specific transcriptomic signatures for drug discovery

Application of Medical Knowledge Graphs in Cardiology and Cardiovascular Medicine: A Brief Literature Review

Contact Info

Product

Resources

About