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Clinical Knowledge Graph (CKG) is a platform with twofold objective: 1) build a graph database with experimental data and data imported from diverse biomedical databases 2) automate knowledge discovery making use of all the information contained in the graph

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Clinical Knowledge Graph

version: 1.0b1 BETA

A Python project that allows you to analyse proteomics and clinical data, and integrate and mine knowledge from multiple biomedical databases widely used nowadays.

Abstract

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The promise of precision medicine is to deliver personalized treatment based on the unique physiology of each patient. This concept was fueled by the genomic revolution, but it is now evident that integrating other types of omics data, like proteomics, into the clinical decision-making process will be essential to accomplish precision medicine goals. However, quantity and diversity of biomedical data, and the spread of clinically relevant knowledge across myriad biomedical databases and publications makes this exceptionally difficult. To address this, we developed the Clinical Knowledge Graph (CKG), an open source platform currently comprised of more than 16 million nodes and 220 million relationships to represent relevant experimental data, public databases and the literature. The CKG also incorporates the latest statistical and machine learning algorithms, drastically accelerating analysis and interpretation of typical proteomics workflows. We use several biomarker studies to illustrate how the CKG may support, enrich and accelerate clinical decision-making.

Cloning and installing

The setting up of the CKG includes several steps and might take a few hours (if you are building the database from scratch). However, we have prepared documentation and manuals that will guide through every step. To get a copy of the GitHub repository on your local machine, please open a terminal windown and run:

$ git clone https://github.com/MannLabs/CKG.git

This will create a new folder named "CKG" on your current location. To access the documentation, use the ReadTheDocs link above, or open the html version stored in the CKG folder CKG/docs/build/html/index.html. After this, follow the instructions in "First Steps" and "Getting Started".

Warning

If git is not installed in your machine, please follow this tutorial to install it.

Features

  • Cross-platform: Mac, and Linux are officially supported.
  • Docker container runs all neccessary steps to setup the CKG.

Disclaimer

This resource is intended for research purposes and must not substitute a doctor’s medical judgement or healthcare professional advice.

Important Note

The databases provided within the Clinical Knowledge Graph (CKG) have their own licenses and the use of CKG still requires compliance with these data use restrictions. Please, visit the data sources directly for more information:

Source type Source URL Reference
Database UniProt https://www.uniprot.org/ https://www.ncbi.nlm.nih.gov/pubmed/29425356
Database TISSUES https://tissues.jensenlab.org/ https://www.ncbi.nlm.nih.gov/pubmed/29617745
Database STRING https://string-db.org/ https://www.ncbi.nlm.nih.gov/pubmed/30476243
Database STITCH http://stitch.embl.de/ https://www.ncbi.nlm.nih.gov/pubmed/26590256
Database SMPDB https://smpdb.ca/ https://www.ncbi.nlm.nih.gov/pubmed/24203708
Database SIGNOR https://signor.uniroma2.it/ https://www.ncbi.nlm.nih.gov/pubmed/31665520
Database SIDER http://sideeffects.embl.de/ https://www.ncbi.nlm.nih.gov/pubmed/26481350
Database RefSeq https://www.ncbi.nlm.nih.gov/refseq/ https://www.ncbi.nlm.nih.gov/pubmed/26553804
Database Reactome https://reactome.org/ https://www.ncbi.nlm.nih.gov/pubmed/31691815
Database PhosphoSitePlus https://www.phosphosite.org/ https://www.ncbi.nlm.nih.gov/pubmed/25514926
Database Pfam https://pfam.xfam.org/ https://www.ncbi.nlm.nih.gov/pubmed/30357350
Database OncoKB https://www.oncokb.org/ https://www.ncbi.nlm.nih.gov/pubmed/28890946
Database MutationDs https://www.ebi.ac.uk/intact/resources/datasets#mutationDs https://www.ncbi.nlm.nih.gov/pubmed/30602777
Database Intact https://www.ebi.ac.uk/intact/ https://www.ncbi.nlm.nih.gov/pubmed/24234451
Database HPA https://www.proteinatlas.org/ https://www.ncbi.nlm.nih.gov/pubmed/21572409
Database HMDB https://hmdb.ca/ https://www.ncbi.nlm.nih.gov/pubmed/29140435
Database HGNC https://www.genenames.org/ https://www.ncbi.nlm.nih.gov/pubmed/30304474
Database GwasCatalog https://www.ebi.ac.uk/gwas/ https://www.ncbi.nlm.nih.gov/pubmed/30445434
Database FooDB https://foodb.ca/  
Database DrugBank https://www.drugbank.ca/ https://www.ncbi.nlm.nih.gov/pubmed/29126136
Database DisGeNET https://www.disgenet.org/ https://www.ncbi.nlm.nih.gov/pubmed/25877637
Database DISEASES https://diseases.jensenlab.org/ https://www.ncbi.nlm.nih.gov/pubmed/25484339
Database DGIdb http://www.dgidb.org/ https://www.ncbi.nlm.nih.gov/pubmed/29156001
Database CORUM https://mips.helmholtz-muenchen.de/corum/ https://www.ncbi.nlm.nih.gov/pubmed/30357367
Database Cancer Genome Interpreter https://www.cancergenomeinterpreter.org/ https://www.ncbi.nlm.nih.gov/pubmed/29592813
       
Ontology Disease Ontology https://disease-ontology.org/ https://www.ncbi.nlm.nih.gov/pubmed/30407550
Ontology Brenda Tissue Ontology https://www.brenda-enzymes.org/ontology.php?ontology_id=3 https://www.ncbi.nlm.nih.gov/pubmed/25378310
Ontology Experimental Factor Ontology https://www.ebi.ac.uk/efo/ https://www.ncbi.nlm.nih.gov/pubmed/20200009
Ontology Gene Ontology http://geneontology.org/ https://www.ncbi.nlm.nih.gov/pubmed/27899567
Ontology Human Phenotype Ontology https://hpo.jax.org/ https://www.ncbi.nlm.nih.gov/pubmed/27899602
Ontology SNOMED-CT http://www.snomed.org/ https://www.ncbi.nlm.nih.gov/pubmed/27332304
Ontology Protein Modification Ontology https://www.ebi.ac.uk/ols/ontologies/mod https://www.ncbi.nlm.nih.gov/pubmed/23482073
Ontology Molecular Interactions Ontology https://www.ebi.ac.uk/ols/ontologies/mi https://www.ncbi.nlm.nih.gov/pubmed/23482073
Ontology Mass Spectrometry Ontology https://www.ebi.ac.uk/ols/ontologies/ms https://www.ncbi.nlm.nih.gov/pubmed/23482073

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Clinical Knowledge Graph (CKG) is a platform with twofold objective: 1) build a graph database with experimental data and data imported from diverse biomedical databases 2) automate knowledge discovery making use of all the information contained in the graph

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