Evidence map›Paper›PMID 36852282›Full record

ArticleiScience2023

Using biological constraints to improve prediction in precision oncology.

Mohamed Omar, Wikum Dinalankara, Lotte Mulder, Tendai Coady, Claudio Zanettini, Eddie Luidy Imada, Laurent Younes, Donald Geman, Luigi Marchionni

Open access · goldAbstract read
In one paragraph

Article in iScience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
1.5field-weighted citation impact, top 18% of its field
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

8 citing papers in PubMed, 7 citations in OpenAlex.

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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

9 authors at 3 institutions in 2 countries.

Mohamed OmarDepartment of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY 10065, USA.
Wikum DinalankaraDepartment of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY 10065, USA.
Lotte MulderTechnical University Delft, 2628 CD Delft, the Netherlands.
Tendai CoadyDepartment of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY 10065, USA.
Claudio ZanettiniDepartment of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY 10065, USA.
Eddie Luidy ImadaDepartment of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY 10065, USA.
Laurent YounesDepartment of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD 21218, USA.
Donald GemanDepartment of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD 21218, USA.
Luigi MarchionniDepartment of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY 10065, USA.
Cornell University · USJohns Hopkins University · USDelft University of Technology · NL

Funding

Hardwiring Mechanism into Predicting Cancer Phenotypes by Computational LearningR01CA200859 · NCI · WEILL MEDICAL COLL OF CORNELL UNIV · PI MARCHIONNI, LUIGI · 2016 to 2020
$1.9M
NCI NIH HHS R01 CA200859
6 · The paper itself

Abstract

Many gene signatures have been developed by applying machine learning (ML) on

Indexed as

CancerMachine learningOmicsPrecision medicine

Identifiers

PMID36852282
PMCPMC9958363
OpenAlexW4318957109

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.