A knowledge graph to interpret clinical proteomics data

Santos, Alberto; Colaço, Ana R.; Nielsen, Annelaura Bach; Niu, Lili; Strauss, Mike; Geyer, Philipp; Coscia, Fabian; Albrechtsen, Nicolai J. Wewer; Mundt, Filip; Jensen, Lars Juhl; Mann, Matthias

doi:10.1038/s41587-021-01145-6

Cited by 147 publications

(114 citation statements)

References 86 publications

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“…For scientists with programming experience several options exist, including the Cytoscape API [93], Python libraries like NetworkX [94] and graphviz (https://graphviz.readthedocs.io), the R library network [95], or igraph (https://igraph.org), which is available in both languages. A more specialized tool for clinical proteomics that aims to capture comprehensive prior knowledge is the clinical knowledge graph (CKG) [96].…”

Section: Visualization Of Proteomics Datamentioning

confidence: 99%

“…Using a combination of the scientific Python stack, the generalized visualization libraries and web engines, several visualization tools and resource pages for the proteomics field have already been created [46,58,96,102‐104].…”

Section: Custom Programmatic Data Visualizationmentioning

confidence: 99%

“…While uploading datasets to repositories is generally mandatory nowadays, data can be made even more accessible by providing a dedicated online resource or even an analysis service with embedded interactive visualizations. Python provides several libraries that integrate data analysis and visualization capabil- Using a combination of the scientific Python stack, the generalized visualization libraries and web engines, several visualization tools and resource pages for the proteomics field have already been created [46,58,96,[102][103][104].…”

Section: Proteomics Data Visualization In Pythonmentioning

confidence: 99%

See 2 more Smart Citations

A practical guide to interpreting and generating bottom‐up proteomics data visualizations

2022

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Mass-spectrometry based bottom-up proteomics is the main method to analyze proteomes comprehensively and the rapid evolution of instrumentation and data analysis has made the technology widely available. Data visualization is an integral part of the analysis process and it is crucial for the communication of results. This is a major challenge due to the immense complexity of MS data. In this review, we provide an overview of commonly used visualizations, starting with raw data of traditional and novel MS technologies, then basic peptide and protein level analyses, and finally visualization of highly complex datasets and networks. We specifically provide guidance on how to critically interpret and discuss the multitude of different proteomics data visualizations. Furthermore, we highlight Python-based libraries and other open science tools that can be applied for independent and transparent generation of customized visualizations. To further encourage programmatic data visualization, we provide the Python code used to generate all data figures in this review on GitHub (https: //github.com/MannLabs/ProteomicsVisualization).

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Section: Visualization Of Proteomics Datamentioning

confidence: 99%

Section: Custom Programmatic Data Visualizationmentioning

confidence: 99%

Section: Proteomics Data Visualization In Pythonmentioning

confidence: 99%

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A practical guide to interpreting and generating bottom‐up proteomics data visualizations

2022

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“…blood pressure taking process at some specific time point). This graph-based structure allows for further exploitation of biomedical knowledge and relations within the heterogenous data, for example for predicting new relations between terms such as genes, diseases, drugs, or symptoms (45,46).…”

Section: Resultsmentioning

confidence: 99%

Digital twins and hybrid modelling for simulation of physiological variables and stroke risk

Herrgårdh

Hunter²,

Tunedal

et al. 2022

Preprint

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One of the more interesting ideas for achieving personalized, preventive, and participatory medicine is the concept of a digital twin. A digital twin is a personalized computer model of a patient. So far, digital twins have been constructed using either mechanistic models, which can simulate the trajectory of physiological and biochemical processes in a person, or using machine learning models, which for example can be used to estimate the risk of having a stroke given a cross-section profile at a given timepoint. These two modelling approaches have complementary strengths which can be combined into a hybrid model. However, even though hybrid modelling combining mechanistic modelling and machine learning have been proposed, there are few, if any, real examples of hybrid digital twins available. We now present such a hybrid model for the simulation of ischemic stroke. On the mechanistic side, we develop a new model for blood pressure and integrate this with an existing multi-level and multi-timescale model for the development of type 2 diabetes. This mechanistic model can simulate the evolution of known physiological risk factors (such as weight, diabetes development, and blood pressure) through time, under different intervention scenarios, involving a change in diet, exercise, and certain medications. These forecast trajectories of the physiological risk factors are then used by a machine learning model to calculate the 5-year risk of stroke, which thus also can be calculated for each timepoint in the simulated scenarios. We discuss and illustrate practical issues with clinical implementation, such as data gathering and harmonization. By improving patients' understanding of their body and health, the digital twin can serve as a valuable tool for patient education and as a conversation aid during the clinical encounter. As such, it can facilitate shared decision-making, promote behavior change towards a healthy lifestyle, and improve adherence to prescribed medications.

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“…The work we present here has several avenues for future improvement to generate more comprehensive information about biological structures. Integrating multiple data modalities and patient data is critical to the complete understanding of disease processes, and the correct integration relies on advanced bioinformatics tools 47,48 . The full integration of spatially resolved proteomic, metabolic, lipidomic and transcriptomic data, along with high-resolution imaging, has the potential to reveal further insights into the spatial dimension of biological processes.…”

Section: Discussionmentioning

confidence: 99%

Deep topographic proteomics of a human brain tumour

Davis

Scott

Oetjen

et al. 2022

Preprint

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Cellular protein expression profiles within tissues are key to understanding disease pathology, and their spatial organisation determines cellular function. To precisely define molecular phenotypes in the spatial context of tissue, there is a need for unbiased, quantitative technology capable of mapping the expression of many hundreds to thousands of proteins within tissue structures. Here, we present a workflow for spatially resolved, quantitative proteomics of tissue that generates maps of protein expression across a tissue slice derived from a human atypical teratoid-rhabdoid tumour (AT/RT). We employ spatially-aware statistical methods that do not require prior knowledge of tissue structure to highlight proteins and pathways with varying spatial abundance patterns. We identify novel aspects of AT/RT biology that map onto the brain-tumour interface. Overall, this work informs on methods for spatially resolved deep proteo-phenotyping of tissue heterogeneity. Advanced spatially resolved tissue proteomics will push the boundaries of understanding tissue biology and pathology at the molecular level.

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A knowledge graph to interpret clinical proteomics data

Cited by 147 publications

References 86 publications

A practical guide to interpreting and generating bottom‐up proteomics data visualizations

A practical guide to interpreting and generating bottom‐up proteomics data visualizations

Digital twins and hybrid modelling for simulation of physiological variables and stroke risk

Deep topographic proteomics of a human brain tumour

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