Evidence map›Paper›PMID 40188496›Full record

ArticleBriefings in bioinformatics2025

FusionPub, a therapeutic landscape of human fusion genes.

Himansu Kumar, Abayomi Adegunlehin, Zikang Chen, Pora Kim

Abstract read
In one paragraph

Article in Briefings in bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

4 authors.

Himansu KumarDepartment of Bioinformatics and Systems Medicine, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, 7000 Fannin St., Houston, TX 77030, United States.ORCID 0000-0002-4335-4517
Abayomi AdegunlehinDepartment of Health Data Science and Artificial Intelligence, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, 7000 Fannin St., Houston, TX 77030, United States.
Zikang ChenDepartment of Computer Science, Rice University, 6100 Main St, Houston, TX 77005, United States.
Pora KimDepartment of Bioinformatics and Systems Medicine, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, 7000 Fannin St., Houston, TX 77030, United States.ORCID 0000-0002-8321-6864

Funding

Functional annotation of new genes aided by deep learningR35GM138184 · NIGMS · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI KIM, PORA · 2020 to 2024
$1.7M
Cancer Prevention and Research Institute of Texas RP210045NIGMS NIH HHS R35 GM138184NIH HHS R35GM138184
6 · The paper itself

Abstract

To advance the development of fusion protein-targeting therapeutics, it is crucial to understand how patients with fusion oncoproteins have been treated and what small molecules have been studied. To fill this gap, we developed FusionPub, a knowledgebase of the therapeutic landscape of human fusion genes. We searched PubMed abstracts for 107K human fusion genes with 14K drugs. We also searched PubMed abstracts for 18K human fusion proteins, considering all gene synonyms of individual partner genes with 14K drugs. After manual curation, we found 17 342 records from 9623 PubMed abstracts. For these fusion genes and drugs, we provide a summary sentence of their relationship using the generative AI, details of the drugs, clinical trial records, common drug records from patient treatment history, etc. FusionPub will be a valuable and unique resource providing knowledge of the development of fusion gene targeting therapeutics.

Indexed as

Databases, GeneticNeoplasmsOncogene Proteins, FusionHumansKnowledge BasesOncogene Proteins, FusionClinicalTrials.govdrugfusion genePubMedtherapeutics

Identifiers

PMID40188496
PMCPMC11972630

What OpenQuestion holds

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LicenceCC BY-NC
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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.