Evidence map›Paper›PMID 42495002›Full record

ArticleComputational and structural biotechnology journal2026

BlendSplice: A Frequency-Blended Generative Framework for

Espoir Kabanga, Seonil Jee, Arnout Van Messem, Wesley De Neve

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Computational and structural biotechnology journal, 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

4 authors.

Espoir KabangaIDLab, Department of Electronics and Information Systems, Ghent University, Ghent 9000, Belgium.ORCID https://orcid.org/0000-0002-9523-826X
Seonil JeeCenter for Biosystems and Biotech Data Science, Ghent University Global Campus, Incheon 21985, Republic of Korea.
Arnout Van MessemDepartment of Mathematics, Université de Liège, Liège 4000, Belgium.ORCID https://orcid.org/0000-0001-8545-7437
Wesley De NeveIDLab, Department of Electronics and Information Systems, Ghent University, Ghent 9000, Belgium.ORCID https://orcid.org/0000-0002-8190-3839

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Generative models for biological sequences face challenges in balancing sequence realism with diversity. We investigated whether posttraining frequency blending, combining model-learned distributions with empirical nucleotide priors, can improve synthetic-sequence quality across diverse generative architectures. We present BlendSplice, a frequency-blended generative framework for the

Identifiers

PMID42495002
PMCPMC13392282

What OpenQuestion holds

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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.