Evidence map›Paper›PMID 30821320›Full record

ArticleBioinformatics (Oxford, England)2019

FORESEE: a tool for the systematic comparison of translational drug response modeling pipelines.

Lisa-Katrin Turnhoff, Ali Hadizadeh Esfahani, Maryam Montazeri, Nina Kusch, Andreas Schuppert

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–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

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. 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.

Lisa-Katrin TurnhoffJoint Research Center for Computational Biomedicine (JRC-COMBINE), RWTH Aachen University, Aachen, Germany.
Ali Hadizadeh EsfahaniJoint Research Center for Computational Biomedicine (JRC-COMBINE), RWTH Aachen University, Aachen, Germany.
Maryam MontazeriJoint Research Center for Computational Biomedicine (JRC-COMBINE), RWTH Aachen University, Aachen, Germany.
Nina KuschJoint Research Center for Computational Biomedicine (JRC-COMBINE), RWTH Aachen University, Aachen, Germany.
Andreas SchuppertJoint Research Center for Computational Biomedicine (JRC-COMBINE), RWTH Aachen University, Aachen, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

summaryTranslational models that utilize omics data generated in in vitro studies to predict the drug efficacy of anti-cancer compounds in patients are highly distinct, which complicates the benchmarking process for new computational approaches. In reaction to this, we introduce the uniFied translatiOnal dRug rESponsE prEdiction platform FORESEE, an open-source R-package. FORESEE not only provides a uniform data format for public cell line and patient datasets, but also establishes a standardized environment for drug response prediction pipelines, incorporating various state-of-the-art pre-processing methods, model training algorithms and validation techniques. The modular implementation of individual elements of the pipeline facilitates a straightforward development of combinatorial models, which can be used to re-evaluate and improve already existing pipelines as well as to develop new ones. AVAILABILITY AND IMPLEMENTATION: FORESEE is licensed under GNU General Public License v3.0 and available at https://github.com/JRC-COMBINE/FORESEE and https://doi.org/10.17605/OSF.IO/RF6QK, and provides vignettes for documentation and application both online and in the Supplementary Files 2 and 3. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Indexed as

GenomicsSoftwareAlgorithmsDrug DesignGene Expression

Identifiers

PMID30821320
PMCPMC6761955

What OpenQuestion holds

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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.