Evidence map›Paper›PMID 33155717›Full record

ArticleBiometrical journal. Biometrische Zeitschrift2021

Drug sensitivity prediction with normal inverse Gaussian shrinkage informed by external data.

Magnus M Münch, Mark A van de Wiel, Sylvia Richardson, Gwenaël G R Leday

Abstract read
In one paragraph

Article in Biometrical journal. Biometrische Zeitschrift, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

4 authors.

Magnus M MünchDepartment of Epidemiology & Biostatistics, Amsterdam UMC, VU University, Amsterdam, The Netherlands.ORCID 0000-0001-9659-2044
Mark A van de WielDepartment of Epidemiology & Biostatistics, Amsterdam UMC, VU University, Amsterdam, The Netherlands.
Sylvia RichardsonMRC Biostatistics Unit, University of Cambridge, Cambridge Institute of Public Health, Forvie Site, Robinson Way, Cambridge Biomedical Campus, Cambridge, United Kingdom.
Gwenaël G R LedayMRC Biostatistics Unit, University of Cambridge, Cambridge Institute of Public Health, Forvie Site, Robinson Way, Cambridge Biomedical Campus, Cambridge, United Kingdom.

Funding

Amsterdam Public Health Institute's Methodology Program travel grantMedical Research Council MC_UU_00002/10
6 · The paper itself

Abstract

In precision medicine, a common problem is drug sensitivity prediction from cancer tissue cell lines. These types of problems entail modelling multivariate drug responses on high-dimensional molecular feature sets in typically >1000 cell lines. The dimensions of the problem require specialised models and estimation methods. In addition, external information on both the drugs and the features is often available. We propose to model the drug responses through a linear regression with shrinkage enforced through a normal inverse Gaussian prior. We let the prior depend on the external information, and estimate the model and external information dependence in an empirical-variational Bayes framework. We demonstrate the usefulness of this model in both a simulated setting and in the publicly available Genomics of Drug Sensitivity in Cancer data.

Indexed as

GenomicsPharmaceutical PreparationsBayes TheoremNormal DistributionPrecision MedicinePharmaceutical Preparationsdrug sensitivityempirical BayesGenomics of Drug Sensitivity in Cancer (GDSC)variational Bayes

Identifiers

PMID33155717
PMCPMC7891636

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Registered trials

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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.