Democratizing knowledge representation with BioCypher

Article Computer Science, Artificial Intelligence

Knowledge graphs as tools for explainable machine learning: A survey

Ilaria Tiddi et al.

Summary: This paper provides an extensive overview of the utilization of knowledge graphs in Explainable Machine Learning, highlighting the potential for more meaningful and trustworthy explanations. The integration of knowledge graphs shows promise in enhancing the understandability, reactivity, and accuracy of machine learning systems, while also posing challenges in handling noise and extracting knowledge efficiently for future research.

ARTIFICIAL INTELLIGENCE (2022)

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Article Biotechnology & Applied Microbiology

A knowledge graph to interpret clinical proteomics data

Alberto Santos et al.

Summary: A knowledge graph platform integrates proteomics with other omics data and biomedical databases, providing support for precision medicine. The Clinical Knowledge Graph (CKG) is a flexible data model that can accommodate a large number of nodes and relationships, accelerating the analysis and interpretation of proteomics data. By incorporating CKG, clinical decision-making can be informed and proteomics data can be enriched.

NATURE BIOTECHNOLOGY (2022)

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Review Biochemical Research Methods

A review of biomedical datasets relating to drug discovery: a knowledge graph perspective

Stephen Bonner et al.

Summary: This review provides a detailed introduction to publicly available data sources suitable for constructing drug discovery focused knowledge graphs (KGs). It aims to guide machine learning and KG practitioners who are interested in applying new techniques to the drug discovery field but may lack knowledge of relevant data sources. The review includes a comparative analysis of existing public drug discovery KGs, evaluation of motivating case studies, and raises challenges and future research directions in the domain. It hopes to motivate the use of KGs in addressing key and emerging questions in drug discovery.

BRIEFINGS IN BIOINFORMATICS (2022)

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Article Engineering, Biomedical

Graph representation learning in biomedicine and healthcare

Michelle M. Li et al.

Summary: This Perspective discusses the use of representation learning, particularly graph representation learning, in biomedical and healthcare applications. It argues that graph representation learning will continue to advance machine learning in the fields of medicine and healthcare, and outlines potential applications and directions for development.

NATURE BIOMEDICAL ENGINEERING (2022)

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Article Biochemistry & Molecular Biology

CROssBAR: comprehensive resource of biomedical relations with knowledge graph representations

Tunca Dogan et al.

Summary: By developing a new data integration/representation methodology and constructing a biological data resource, we address the issue of lack of connection between data produced using different technologies, creating a comprehensive system that provides users with easy-to-interpret biological knowledge graphs.

NUCLEIC ACIDS RESEARCH (2021)

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Article Multidisciplinary Sciences