Protocol for predicting suppressors of cell-death pathways based on transcriptomic and vulnerability data

Here, we present a computational protocol for predicting repressors of cancer cell death by combining vulnerability and transcriptomic responses to cell death inducers. We describe steps for calculating a set of predictors for each gene and aggregating them into a single metric, ranking the genes according to their predictive power. We then detail procedures for selecting candidate genes for experimental validation based on this ranking and other considerations. This protocol enables the identification of several experimentally validated cell death repressors. For complete details on the use and execution of this protocol, please refer to Vinik et al.<sup>1</sup>.

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Publication Details

Journal
STAR Protocols
Published
2025-05-29
DOI
https://doi.org/10.1016/j.xpro.2025.103855
Citations
1
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
1.50

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article

Protocol for predicting suppressors of cell-death pathways based on transcriptomic and vulnerability data

Yaron Vinik, Sima Lev, Avi Maimon
1 citations
STAR Protocols
Computational Drug Discovery Methods
1.50
article

Protocol for predicting suppressors of cell-death pathways based on transcriptomic and vulnerability data

Yaron Vinik, Sima Lev, Avi Maimon
article en
1 citations

Abstract

Here, we present a computational protocol for predicting repressors of cancer cell death by combining vulnerability and transcriptomic responses to cell death inducers. We describe steps for calculating a set of predictors for each gene and aggregating them into a single metric, ranking the genes according to their predictive power. We then detail procedures for selecting candidate genes for experimental validation based on this ranking and other considerations. This protocol enables the identification of several experimentally validated cell death repressors. For complete details on the use and execution of this protocol, please refer to Vinik et al.<sup>1</sup>.

STAR ProtocolsVol. 6(2)
Weizmann Institute of Science (IL)
Merck KGaA
Openalex Percentile: Top 16%
Computational Drug Discovery Methods
1.50
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