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Individual-specific networks for prediction modelling – A scoping review of methods

BACKGROUND: Recent advances in biotechnology enable the acquisition of high-dimensional data on individuals, posing challenges for prediction models which traditionally use covariates such as clinical patient characteristics. Alternative forms of covariate representations for the features derived fr...

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Autores principales: Gregorich, Mariella, Melograna, Federico, Sunqvist, Martina, Michiels, Stefan, Van Steen, Kristel, Heinze, Georg
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8898441/
https://www.ncbi.nlm.nih.gov/pubmed/35249534
http://dx.doi.org/10.1186/s12874-022-01544-6
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author Gregorich, Mariella
Melograna, Federico
Sunqvist, Martina
Michiels, Stefan
Van Steen, Kristel
Heinze, Georg
author_facet Gregorich, Mariella
Melograna, Federico
Sunqvist, Martina
Michiels, Stefan
Van Steen, Kristel
Heinze, Georg
author_sort Gregorich, Mariella
collection PubMed
description BACKGROUND: Recent advances in biotechnology enable the acquisition of high-dimensional data on individuals, posing challenges for prediction models which traditionally use covariates such as clinical patient characteristics. Alternative forms of covariate representations for the features derived from these modern data modalities should be considered that can utilize their intrinsic interconnection. The connectivity information between these features can be represented as an individual-specific network defined by a set of nodes and edges, the strength of which can vary from individual to individual. Global or local graph-theoretical features describing the network may constitute potential prognostic biomarkers instead of or in addition to traditional covariates and may replace the often unsuccessful search for individual biomarkers in a high-dimensional predictor space. METHODS: We conducted a scoping review to identify, collate and critically appraise the state-of-art in the use of individual-specific networks for prediction modelling in medicine and applied health research, published during 2000–2020 in the electronic databases PubMed, Scopus and Embase. RESULTS: Our scoping review revealed the main application areas namely neurology and pathopsychology, followed by cancer research, cardiology and pathology (N = 148). Network construction was mainly based on Pearson correlation coefficients of repeated measurements, but also alternative approaches (e.g. partial correlation, visibility graphs) were found. For covariates measured only once per individual, network construction was mostly based on quantifying an individual’s contribution to the overall group-level structure. Despite the multitude of identified methodological approaches for individual-specific network inference, the number of studies that were intended to enable the prediction of clinical outcomes for future individuals was quite limited, and most of the models served as proof of concept that network characteristics can in principle be useful for prediction. CONCLUSION: The current body of research clearly demonstrates the value of individual-specific network analysis for prediction modelling, but it has not yet been considered as a general tool outside the current areas of application. More methodological research is still needed on well-founded strategies for network inference, especially on adequate network sparsification and outcome-guided graph-theoretical feature extraction and selection, and on how networks can be exploited efficiently for prediction modelling. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-022-01544-6.
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spelling pubmed-88984412022-03-16 Individual-specific networks for prediction modelling – A scoping review of methods Gregorich, Mariella Melograna, Federico Sunqvist, Martina Michiels, Stefan Van Steen, Kristel Heinze, Georg BMC Med Res Methodol Research BACKGROUND: Recent advances in biotechnology enable the acquisition of high-dimensional data on individuals, posing challenges for prediction models which traditionally use covariates such as clinical patient characteristics. Alternative forms of covariate representations for the features derived from these modern data modalities should be considered that can utilize their intrinsic interconnection. The connectivity information between these features can be represented as an individual-specific network defined by a set of nodes and edges, the strength of which can vary from individual to individual. Global or local graph-theoretical features describing the network may constitute potential prognostic biomarkers instead of or in addition to traditional covariates and may replace the often unsuccessful search for individual biomarkers in a high-dimensional predictor space. METHODS: We conducted a scoping review to identify, collate and critically appraise the state-of-art in the use of individual-specific networks for prediction modelling in medicine and applied health research, published during 2000–2020 in the electronic databases PubMed, Scopus and Embase. RESULTS: Our scoping review revealed the main application areas namely neurology and pathopsychology, followed by cancer research, cardiology and pathology (N = 148). Network construction was mainly based on Pearson correlation coefficients of repeated measurements, but also alternative approaches (e.g. partial correlation, visibility graphs) were found. For covariates measured only once per individual, network construction was mostly based on quantifying an individual’s contribution to the overall group-level structure. Despite the multitude of identified methodological approaches for individual-specific network inference, the number of studies that were intended to enable the prediction of clinical outcomes for future individuals was quite limited, and most of the models served as proof of concept that network characteristics can in principle be useful for prediction. CONCLUSION: The current body of research clearly demonstrates the value of individual-specific network analysis for prediction modelling, but it has not yet been considered as a general tool outside the current areas of application. More methodological research is still needed on well-founded strategies for network inference, especially on adequate network sparsification and outcome-guided graph-theoretical feature extraction and selection, and on how networks can be exploited efficiently for prediction modelling. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-022-01544-6. BioMed Central 2022-03-06 /pmc/articles/PMC8898441/ /pubmed/35249534 http://dx.doi.org/10.1186/s12874-022-01544-6 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Gregorich, Mariella
Melograna, Federico
Sunqvist, Martina
Michiels, Stefan
Van Steen, Kristel
Heinze, Georg
Individual-specific networks for prediction modelling – A scoping review of methods
title Individual-specific networks for prediction modelling – A scoping review of methods
title_full Individual-specific networks for prediction modelling – A scoping review of methods
title_fullStr Individual-specific networks for prediction modelling – A scoping review of methods
title_full_unstemmed Individual-specific networks for prediction modelling – A scoping review of methods
title_short Individual-specific networks for prediction modelling – A scoping review of methods
title_sort individual-specific networks for prediction modelling – a scoping review of methods
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8898441/
https://www.ncbi.nlm.nih.gov/pubmed/35249534
http://dx.doi.org/10.1186/s12874-022-01544-6
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