Evidence map›Paper›PMID 42282693›Full record

ArticlebioRxiv : the preprint server for biology2026

GeneKnow: An auditable AI framework for source-grounded biological evidence synthesis.

Hongpan Zhang, Bhummanat Sittipongpittaya, Chongzhi Zang

Abstract readPreprint
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

3 authors.

Hongpan ZhangDepartment of Genome Sciences, University of Virginia, Charlottesville, VA, USA.ORCID 0000-0003-0932-176X
Bhummanat SittipongpittayaDepartment of Genome Sciences, University of Virginia, Charlottesville, VA, USA.
Chongzhi ZangDepartment of Genome Sciences, University of Virginia, Charlottesville, VA, USA.ORCID 0000-0003-4812-3627

Funding

Integrative computational models for functional epigenomics and transcriptional regulationR35GM133712 · NIGMS · UNIVERSITY OF VIRGINIA · PI Chongzhi Zang · 2019 to 2026
$3.4M
NIGMS NIH HHS R35 GM133712
6 · The paper itself

Abstract

Biological function is context dependent, yet synthesizing evidence for a gene's function in a defined cell type, disease, or perturbation remains labor-intensive. Here we present GeneKnow, an auditable artificial intelligence (AI) framework for source-grounded biological evidence synthesis. GeneKnow separates evidence-critical operations, including literature retrieval, passage selection, provenance tracking, and bibliography construction, from generative steps that are constrained to semantic analysis and literature synthesis. GeneKnow supports multi-paper discovery and single-paper inspection while preserving links from synthesized claims to source passages, generating trustworthy syntheses without fabricated citations and minimizing hallucinations. Systematic benchmarking showed that GeneKnow achieved higher claim support and citation fidelity than leading general-purpose and scientific AI systems. These results demonstrate that an AI system with controlled division of labor between deterministic and generative components can substantially improve the fidelity and auditability of biomedical literature synthesis.

Identifiers

PMID42282693
PMCPMC13252112

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.