Evidence map›Paper›PMID 35474966›Full record

ArticleCell reports methods2022

Extracting functional insights from loss-of-function screens using deep link prediction.

Pieter-Paul Strybol, Maarten Larmuseau, Louise de Schaetzen van Brienen, Tim Van den Bulcke, Kathleen Marchal

Abstract read
In one paragraph

Article in Cell reports methods, 2022. 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

5 authors.

Pieter-Paul StrybolDepartment of Plant Biotechnology and Bioinformatics, Department of Information Technology, IDLab, imec, iGent Toren, 9000 Gent, Belgium.
Maarten LarmuseauDepartment of Plant Biotechnology and Bioinformatics, Department of Information Technology, IDLab, imec, iGent Toren, 9000 Gent, Belgium.
Louise de Schaetzen van BrienenDepartment of Plant Biotechnology and Bioinformatics, Department of Information Technology, IDLab, imec, iGent Toren, 9000 Gent, Belgium.
Tim Van den BulckeGalapagos NV, Generaal De Wittelaan L11 A3, 2800 Mechelen, Belgium.
Kathleen MarchalDepartment of Plant Biotechnology and Bioinformatics, Department of Information Technology, IDLab, imec, iGent Toren, 9000 Gent, Belgium.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We present deep link prediction (DLP), a method for the interpretation of loss-of-function screens. Our approach uses representation-based link prediction to reprioritize phenotypic readouts by integrating screening experiments with gene-gene interaction networks. We validate on 2 different loss-of-function technologies, RNAi and CRISPR, using datasets obtained from DepMap. Extensive benchmarking shows that DLP-DeepWalk outperforms other methods in recovering cell-specific dependencies, achieving an average precision well above 90% across 7 different cancer types and on both RNAi and CRISPR data. We show that the genes ranked highest by DLP-DeepWalk are appreciably more enriched in drug targets compared to the ranking based on original screening scores. Interestingly, this enrichment is more pronounced on RNAi data compared to CRISPR data, consistent with the greater inherent noise of RNAi screens. Finally, we demonstrate how DLP-DeepWalk can infer the molecular mechanism through which putative targets trigger cell line mortality.

Indexed as

NeoplasmsCell LineHumansRNA Interferencebioinformaticscancer cell linesCRISPR screeningdeep learningdrug targetsfunctional screeninglink predictionmachine learningPPI networkssystems biology

Identifiers

PMID35474966
PMCPMC9017186

What OpenQuestion holds

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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.