The OREGANO knowledge graph for computational drug repurposing

Scientific Data, Journal Year: 2023, Volume and Issue: 10(1)

Published: Dec. 6, 2023

Abstract Drug repositioning is a faster and more affordable solution than traditional drug discovery approaches. From this perspective, computational using knowledge graphs very promising direction. Knowledge constructed from data information can be used to generate hypotheses (molecule/drug - target links) through link prediction machine learning algorithms. However, it remains rare have holistically graph the broadest possible features characteristics, which freely available community. The OREGANO aims at filling gap. purpose of paper present graph, includes natural compounds related data. was developed scratch by retrieving directly sources integrated. We therefore designed expected model proposed method for merging nodes between different sources, finally, were cleaned. as well source codes ETL process, are openly on GitHub project ( https://gitub.u-bordeaux.fr/erias/oregano ).

Language: Английский

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Advancing drug–target interaction prediction: a comprehensive graph-based approach integrating knowledge graph embedding and ProtBert pretraining

BMC Bioinformatics, Journal Year: 2023, Volume and Issue: 24(1)

Published: Dec. 19, 2023

The pharmaceutical field faces a significant challenge in validating drug target interactions (DTIs) due to the time and cost involved, leading only fraction being experimentally verified. To expedite discovery, accurate computational methods are essential for predicting potential interactions. Recently, machine learning techniques, particularly graph-based methods, have gained prominence. These utilize networks of drugs targets, employing knowledge graph embedding (KGE) represent structured information from graphs continuous vector space. This phenomenon highlights growing inclination topologies as means improve precision DTIs, hence addressing pressing requirement effective methodologies discovery.

Language: Английский

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Connecting electronic health records to a biomedical knowledge graph to link clinical phenotypes and molecular endotypes in atopic dermatitis

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 24, 2025

Precision medicine is defined by the U.S. Food & Drug Administration as "an innovative approach to tailoring disease prevention and treatment that considers differences in people's genes, environments, lifestyles". To succeed providing personalized patients, it will be necessary integrate medical, biological molecular data order identify all complex subtypes understand their pathobiological mechanism. Since biomedical knowledge graphs (BKGs) are limited integration of prior do not real-world (RWD) would allow for incorporation patient level information, we propose a first step towards using RWD, BKGs graph machine learning (ML) enable fully integrated precision strategy. In this study, established link between RWD BKG. Our methodology introduced novel representation ML applied This facilitated interpretation extension findings, particularly subtype identification with contained We our deepen understanding atopic dermatitis, condition underlying pathophysiological Through analysis, identified seven subgroups patients each characterized clinical genomic characteristics.

Language: Английский

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Knowledge Graph Applications and Multi-Relation Learning for Drug Repurposing: A Scoping Review

Computational Biology and Chemistry, Journal Year: 2025, Volume and Issue: 115, P. 108364 - 108364

Published: Jan. 31, 2025

Language: Английский

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A knowledge graph for crop diseases and pests in China

Scientific Data, Journal Year: 2025, Volume and Issue: 12(1)

Published: Feb. 6, 2025

Abstract A standardized representation and sharing of crop disease pest data is crucial for enhancing yields, especially in China, which features vast cultivation areas complex agricultural ecosystems. knowledge graph diseases pests, acting as a repository entities relationships, conceptually achieving unified management. However, there currently lack graphs specifically designed this field. In paper, we propose CropDP-KG, pests leverages natural language processing techniques to analyze from the Chinese image-text database. CropDP-KG covers relevant information on featuring 8 primary such diseases, symptoms, crops, organized into 7 relationships occurrence locations, affected parts suitable temperature. total, it includes 13,840 21,961 relationships. case studies presented research, also show versatile application CropDP, namely service system, have released its codebase under an open-source license. The content paper provides guide users build their own graphs, aiming help them effectively reuse extend they create.

Language: Английский

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Drug repurposing for Alzheimer’s disease and other neurodegenerative disorders

Nature Communications, Journal Year: 2025, Volume and Issue: 16(1)

Published: Feb. 19, 2025

Repurposed drugs provide a rich source of potential therapies for Alzheimer's disease (AD) and other neurodegenerative disorders (NDD). have information from non-clinical studies, phase 1 dosing, safety tolerability data collected with the original indication. Computational approaches, "omic" drug databases, electronic medical records help identify candidate therapies. Generic repurposed agents lack intellectual property protection are rarely advanced to late-stage trials AD/NDD. In this review we define repurposing, describe advantages challenges offer strategies overcoming obstacles, key contributions repurposing development ecosystem. review, authors discuss obstacles development.

Language: Английский

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An update on knowledge graphs and their current and potential applications in drug discovery

Expert Opinion on Drug Discovery, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 21

Published: April 14, 2025

Knowledge graphs are becoming prominent tools in computational drug discovery. They effectively integrate heterogeneous biomedical data and generate new hypotheses knowledge. This article is based on a literature review using Google Scholar PubMed to retrieve articles existing knowledge relevant the discovery field. The authors compare types of entities, relationships, sources they encompass. Additionally, provide examples their use field discuss potential strategies for advancing this research area. crucial discovery, but construction leads challenges integration consistency. Future should prioritize standardization modeling. More efforts needed diverse types, such as chemical structures epigenetic data, enhance effectiveness. advancements large language models be pursued aid development graphs, intuitive querying capabilities non-expert users, explain -derived predictions, thereby making these more accessible insights interpretable wider audience.

Language: Английский

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An ontology-based knowledge graph for representing interactions involving RNA molecules

Scientific Data, Journal Year: 2024, Volume and Issue: 11(1)

Published: Aug. 22, 2024

The "RNA world" represents a novel frontier for the study of fundamental biological processes and human diseases is paving way development new drugs tailored to each patient's biomolecular characteristics. Although scientific data about coding non-coding RNA molecules are constantly produced available from public repositories, they scattered across different databases centralized, uniform, semantically consistent representation still lacking. We propose RNA-KG, knowledge graph (KG) encompassing RNAs gathered more than 60 databases, integrating functional relationships with genes, proteins, chemicals ontologically grounded biomedical concepts. To develop we first identified, pre-processed, characterized source; next, built meta-graph that provides an ontological description KG by representing all bio-molecular entities medical concepts interest in this domain, as well types interactions connecting them. Finally, leveraged instance-based abstracted model specify alignment according which RNA-KG was generated. can be downloaded formats also queried SPARQL endpoint. A thorough topological analysis resulting heterogeneous further insights into characteristics world". both directly explored visualized, and/or analyzed applying computational methods infer bio-medical its nodes edges. resource easily updated experimental data, specific views overall extracted problem studied.

Language: Английский

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Knowledge Graphs for drug repurposing: a review of databases and methods

Briefings in Bioinformatics, Journal Year: 2024, Volume and Issue: 25(6)

Published: Sept. 12, 2024

Drug repurposing has emerged as a effective and efficient strategy to identify new treatments for variety of diseases. One the most approaches discovering potential drug candidates involves utilization Knowledge Graphs (KGs). This review comprehensively explores some prominent KGs, detailing their structure, data sources, how they facilitate drugs. In addition this paper delves into various artificial intelligence techniques that enhance process repurposing. These methods not only accelerate identification viable but also improve precision predictions by leveraging complex datasets advanced algorithms. Furthermore, importance explainability in is emphasized. Explainability are crucial provide insights reasoning behind AI-generated predictions, thereby increasing trustworthiness transparency process. We will discuss several can be employed validate these ensuring both reliable understandable.

Language: Английский

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Application of artificial intelligence and machine learning in drug repurposing

Progress in molecular biology and translational science, Journal Year: 2024, Volume and Issue: unknown, P. 171 - 211

Published: Jan. 1, 2024

Language: Английский

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