Assessing the merits: an opinion on the effectiveness of simulation techniques in tumor subclonal reconstruction.

Jiaying Lai, Yi Yang, Yunzhou Liu, Robert B Scharpf, Rachel Karchin

Abstract readEditorial

In one paragraph

Article in Bioinformatics advances, 2024. 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.

No citing paper in PubMed yet.

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.

Jiaying LaiInstitute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, United States.ORCID https://orcid.org/0000-0003-3652-1070

Yi YangInstitute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, United States.

Yunzhou LiuInstitute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, United States.

Robert B ScharpfSidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21231, United States.ORCID https://orcid.org/0000-0003-4702-2656

Rachel KarchinInstitute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, United States.ORCID https://orcid.org/0000-0002-5069-1239

Funding

Interrogation of the Impact of Selection on the Evolution of Human PancreaticCancer Precursor LesionsU01CA271273 · NCI · JOHNS HOPKINS UNIVERSITY · PI Elana Fertig, Rachel Karchin · 2022 to 2026

$2.8M

NCI NIH HHS U01 CA271273

6 · The paper itself

Abstract

Summary: Neoplastic tumors originate from a single cell, and their evolution can be traced through lineages characterized by mutations, copy number alterations, and structural variants. These lineages are reconstructed and mapped onto evolutionary trees with algorithmic approaches. However, without ground truth benchmark sets, the validity of an algorithm remains uncertain, limiting potential clinical applicability. With a growing number of algorithms available, there is urgent need for standardized benchmark sets to evaluate their merits. Benchmark sets rely on Availability and implementation: All analysis done in the paper was based on publicly available data from the publication of each accessed tool.

Identifiers

PMID38948008

DOI10.1093/bioadv/vbae094

PMCPMC11213631

What OpenQuestion holds

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LicenceCC BY

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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.