Evidence map›Paper›PMID 37961450›Full record

ArticlemedRxiv : the preprint server for health sciences2023

Characterization of highly active mutational signatures in tumors from a large Chinese population.

Aaron Chevalier, Tao Guo, Natasha Q Gurevich, Jingwen Xu, Masanao Yajima, Joshua D Campbell

Open access · greenAbstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2023. 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, 4 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors at 1 institution in 1 country.

Aaron ChevalierSection of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts.ORCID 0000-0002-3968-9250
Tao GuoSection of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts.ORCID 0009-0005-8960-9203
Natasha Q GurevichSection of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts.ORCID 0000-0002-0747-6840
Jingwen XuSection of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts.ORCID 0009-0001-0114-452X
Masanao YajimaDepartment of Mathematics & Statistics, Boston University, Boston, Massachusetts.ORCID 0000-0002-7583-3707
Joshua D CampbellSection of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts.ORCID 0000-0003-0780-8662
Boston University · US

Funding

Utilizing Bayesian modeling to improve mutational signature inference in large-scale datasetsU01CA253500 · NCI · BOSTON UNIVERSITY MEDICAL CAMPUS · PI CAMPBELL, JOSHUA D, YAJIMA, MASANAO · 2021 to 2023
$1.2M
NCI NIH HHS U01 CA253500
6 · The paper itself

Abstract

The majority of mutational signatures have been characterized in tumors from Western countries and the degree to which mutational signatures are similar or different in Eastern populations has not been fully explored. We leveraged a large-scale clinical sequencing cohort of tumors from a Chinese population containing 25 tumor types and found that the highly active mutational signatures were similar to those previously characterized

Identifiers

PMID37961450
PMCPMC10635259
OpenAlexW4388331282

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.