A knowledge graph to interpret clinical proteomics data

Research output: Contribution to journal › Journal article › Research › peer-review

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A knowledge graph to interpret clinical proteomics data. / Santos, Alberto; Colaço, Ana R.; Nielsen, Annelaura B.; Niu, Lili; Strauss, Maximilian; Geyer, Philipp E.; Coscia, Fabian; Albrechtsen, Nicolai J.Wewer; Mundt, Filip; Jensen, Lars Juhl; Mann, Matthias.

In: Nature Biotechnology, Vol. 40, 2022, p. 692–702.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Santos, A, Colaço, AR, Nielsen, AB, Niu, L, Strauss, M, Geyer, PE, Coscia, F, Albrechtsen, NJW, Mundt, F, Jensen, LJ & Mann, M 2022, 'A knowledge graph to interpret clinical proteomics data', Nature Biotechnology, vol. 40, pp. 692–702. https://doi.org/10.1038/s41587-021-01145-6

APA

Santos, A., Colaço, A. R., Nielsen, A. B., Niu, L., Strauss, M., Geyer, P. E., Coscia, F., Albrechtsen, N. J. W., Mundt, F., Jensen, L. J., & Mann, M. (2022). A knowledge graph to interpret clinical proteomics data. Nature Biotechnology, 40, 692–702. https://doi.org/10.1038/s41587-021-01145-6

Vancouver

Santos A, Colaço AR, Nielsen AB, Niu L, Strauss M, Geyer PE et al. A knowledge graph to interpret clinical proteomics data. Nature Biotechnology. 2022;40:692–702. https://doi.org/10.1038/s41587-021-01145-6

Author

Santos, Alberto ; Colaço, Ana R. ; Nielsen, Annelaura B. ; Niu, Lili ; Strauss, Maximilian ; Geyer, Philipp E. ; Coscia, Fabian ; Albrechtsen, Nicolai J.Wewer ; Mundt, Filip ; Jensen, Lars Juhl ; Mann, Matthias. / A knowledge graph to interpret clinical proteomics data. In: Nature Biotechnology. 2022 ; Vol. 40. pp. 692–702.

Bibtex

@article{907d8f2e112e4f6ba2783f4116a66b06,
title = "A knowledge graph to interpret clinical proteomics data",
abstract = "Implementing precision medicine hinges on the integration of omics data, such as proteomics, into the clinical decision-making process, but the quantity and diversity of biomedical data, and the spread of clinically relevant knowledge across multiple biomedical databases and publications, pose a challenge to data integration. Here we present the Clinical Knowledge Graph (CKG), an open-source platform currently comprising close to 20 million nodes and 220 million relationships that represent relevant experimental data, public databases and literature. The graph structure provides a flexible data model that is easily extendable to new nodes and relationships as new databases become available. The CKG incorporates statistical and machine learning algorithms that accelerate the analysis and interpretation of typical proteomics workflows. Using a set of proof-of-concept biomarker studies, we show how the CKG might augment and enrich proteomics data and help inform clinical decision-making.",
author = "Alberto Santos and Cola{\c c}o, {Ana R.} and Nielsen, {Annelaura B.} and Lili Niu and Maximilian Strauss and Geyer, {Philipp E.} and Fabian Coscia and Albrechtsen, {Nicolai J.Wewer} and Filip Mundt and Jensen, {Lars Juhl} and Matthias Mann",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s).",
year = "2022",
doi = "10.1038/s41587-021-01145-6",
language = "English",
volume = "40",
pages = "692–702",
journal = "Nature Biotechnology",
issn = "1087-0156",
publisher = "nature publishing group",

}

RIS

TY - JOUR

T1 - A knowledge graph to interpret clinical proteomics data

AU - Santos, Alberto

AU - Colaço, Ana R.

AU - Nielsen, Annelaura B.

AU - Niu, Lili

AU - Strauss, Maximilian

AU - Geyer, Philipp E.

AU - Coscia, Fabian

AU - Albrechtsen, Nicolai J.Wewer

AU - Mundt, Filip

AU - Jensen, Lars Juhl

AU - Mann, Matthias

N1 - Publisher Copyright: © 2022, The Author(s).

PY - 2022

Y1 - 2022

N2 - Implementing precision medicine hinges on the integration of omics data, such as proteomics, into the clinical decision-making process, but the quantity and diversity of biomedical data, and the spread of clinically relevant knowledge across multiple biomedical databases and publications, pose a challenge to data integration. Here we present the Clinical Knowledge Graph (CKG), an open-source platform currently comprising close to 20 million nodes and 220 million relationships that represent relevant experimental data, public databases and literature. The graph structure provides a flexible data model that is easily extendable to new nodes and relationships as new databases become available. The CKG incorporates statistical and machine learning algorithms that accelerate the analysis and interpretation of typical proteomics workflows. Using a set of proof-of-concept biomarker studies, we show how the CKG might augment and enrich proteomics data and help inform clinical decision-making.

AB - Implementing precision medicine hinges on the integration of omics data, such as proteomics, into the clinical decision-making process, but the quantity and diversity of biomedical data, and the spread of clinically relevant knowledge across multiple biomedical databases and publications, pose a challenge to data integration. Here we present the Clinical Knowledge Graph (CKG), an open-source platform currently comprising close to 20 million nodes and 220 million relationships that represent relevant experimental data, public databases and literature. The graph structure provides a flexible data model that is easily extendable to new nodes and relationships as new databases become available. The CKG incorporates statistical and machine learning algorithms that accelerate the analysis and interpretation of typical proteomics workflows. Using a set of proof-of-concept biomarker studies, we show how the CKG might augment and enrich proteomics data and help inform clinical decision-making.

U2 - 10.1038/s41587-021-01145-6

DO - 10.1038/s41587-021-01145-6

M3 - Journal article

C2 - 35102292

AN - SCOPUS:85123916062

VL - 40

SP - 692

EP - 702

JO - Nature Biotechnology

JF - Nature Biotechnology

SN - 1087-0156

ER -

ID: 292072660