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Protocol for using Ciclops to build models trained on cross-platform transcriptome data for clinical outcome prediction.

ABSTRACT: Designing robust, generalizable models based on cross-platform data to predict clinical outcomes remains challenging. Building explainable models is important because models may perform differently depending on the conditions of the samples. Here, we describe the use of Ciclops (cross-platform training in clinical outcome predictions), freely available software that can build explainable models to deliver across cross-platform datasets for predicting clinical outcomes. This protocol also utilizes SHAP, a post-training analysis allowing for assessing potential biomarkers of the clinical outcome under study. For complete details on the use and execution of this protocol, please refer to Zhang et al. (2022).

SUBMITTER: Chou E

PROVIDER: S-EPMC9307566 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature

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Protocol for using Ciclops to build models trained on cross-platform transcriptome data for clinical outcome prediction.

Chou Elysia E Zhang Hanrui H Guan Yuanfang Y

STAR protocols 20220720 3

Designing robust, generalizable models based on cross-platform data to predict clinical outcomes remains challenging. Building explainable models is important because models may perform differently depending on the conditions of the samples. Here, we describe the use of Ciclops (cross-platform training in clinical outcome predictions), freely available software that can build explainable models to deliver across cross-platform datasets for predicting clinical outcomes. This protocol also utilize ...[more]

PMID: 35880126

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