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  Gradient tree boosting and network propagation for the identification of pan-cancer survival networks

Thedinga, K., & Herwig, R. (2022). Gradient tree boosting and network propagation for the identification of pan-cancer survival networks. STAR Protocols, 3(2): 101353. doi:10.1016/j.xpro.2022.101353.

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STAR Protocols_Thedinga und Herwig_2022.pdf (Publisher version), 5MB
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STAR Protocols_Thedinga und Herwig_2022.pdf
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© 2022 The Author(s

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 Creators:
Thedinga, Kristina1, Author              
Herwig, Ralf1, Author              
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1Bioinformatics (Ralf Herwig), Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_2385701              

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 Abstract: Cancer survival prediction is typically done with uninterpretable machine learning techniques, e.g., gradient tree boosting. Therefore, additional steps are needed to infer biological plausibility of the predictions. Here, we describe a protocol that combines pan-cancer survival prediction with XGBoost tree- ensemble learning and subsequent propagation of the learned feature weights on protein interaction networks. This protocol is based on TCGA transcriptome data of 8,024 patients from 25 cancer types but can easily be adapted to cancer patient data from other sources. For complete details on the use and execution of this protocol, please refer to Thedinga and Herwig (2022).

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Language(s): eng - English
 Dates: 2022-06-17
 Publication Status: Published online
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 Rev. Type: -
 Identifiers: DOI: 10.1016/j.xpro.2022.101353
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Title: STAR Protocols
Source Genre: Journal
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Publ. Info: Cambridge, MA ; Amsterdam : Cell Press ; Elsevier
Pages: - Volume / Issue: 3 (2) Sequence Number: 101353 Start / End Page: - Identifier: ISSN: 2666-1667
CoNE: https://pure.mpg.de/cone/journals/resource/2666-1667