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

Elysia Chou; Hanrui Zhang; Yuanfang Guan

doi:10.1016/j.xpro.2022.101583

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

STAR Protoc. 2022 Jul 20;3(3):101583. doi: 10.1016/j.xpro.2022.101583. eCollection 2022 Sep 16.

Authors

Elysia Chou¹, Hanrui Zhang¹, Yuanfang Guan²

Affiliations

¹ Department of Computational Medicine and Bioinformatics, Michigan Medicine, University of Michigan, Ann Arbor, MI 48109, USA.
² Department of Computational Medicine and Bioinformatics, Michigan Medicine, University of Michigan, Ann Arbor, MI 48109, USA; Department of Internal Medicine, Michigan Medicine, University of Michigan, Ann Arbor, MI 48109, USA. Electronic address: gyuanfan@umich.edu.

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).

Keywords: Bioinformatics; Clinical Protocol; Genomics; Systems biology.

Publication types

Research Support, U.S. Gov't, Non-P.H.S.
Research Support, N.I.H., Extramural

MeSH terms

Names*
Prognosis
Software
Transcriptome* / genetics

Abstract

Publication types

MeSH terms

Grants and funding