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

Elysia Chou, Hanrui Zhang, Yuanfang Guan

Abstract read

In one paragraph

Article in STAR protocols, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Elysia ChouDepartment of Computational Medicine and Bioinformatics, Michigan Medicine, University of Michigan, Ann Arbor, MI 48109, USA.

Hanrui ZhangDepartment of Computational Medicine and Bioinformatics, Michigan Medicine, University of Michigan, Ann Arbor, MI 48109, USA.

Yuanfang GuanDepartment 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.

Funding

Machine Learning for Drug Response PredictionR35GM133346 · NIGMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Yuanfang Guan · 2019 to 2026

$3.2M

Biomedical Informatics and Data Science Training Program (BIDS-TP)T32GM141746 · NIGMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Ivo D Dinov, RYAN E MILLS · 2021 to 2026

$2.6M

NIGMS NIH HHS R35 GM133346NIGMS NIH HHS T32 GM141746

6 · The paper itself

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).

Indexed as

NamesTranscriptomePrognosisSoftwareBioinformaticsClinical ProtocolGenomicsSystems biology

Identifiers

PMID35880126

DOI10.1016/j.xpro.2022.101583

PMCPMC9307566

What OpenQuestion holds

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LicenceCC BY

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.