Evidence map›Paper›PMID 42051318›Full record

ArticlebioRxiv : the preprint server for biology2026

Identifying Robust Subclonal Structures through Tumor Progression Tree Alignment.

Jacob Gilbert, Chih Hao Wu, Marina Knittel, Alejandro A Schäffer, Salem Malikić, S Cenk Sahinalp

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2026. 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

6 authors.

Jacob GilbertDepartment of Computer Science, University of Maryland, College Park, MD, USA.
Chih Hao WuCancer Data Science Laboratory, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Marina KnittelDepartment of Computer Science and Engineering, UC San Diego, San Diego, CA, USA.
Alejandro A SchäfferCancer Data Science Laboratory, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Salem MalikićCancer Data Science Laboratory, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
S Cenk SahinalpCancer Data Science Laboratory, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Understanding and comparing tumor evolutionary histories is fundamental to cancer genomics. Clonal trees, used to model tumor progression, are rooted, unordered trees in which each node represents a subclone labeled by a set of distinct mutations. To compare two clonal trees, we introduce omlta, the optimal multi-label tree alignment, which removes the minimum number of mutation labels from the trees, so that the remaining trees are isomorphic. Computing omlta is NP-hard. Here, we present an algorithm to compute the omlta, with a running time of

Identifiers

PMID42051318
PMCPMC13119310

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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.