Evidence map›Paper›PMID 39975405›Full record

ArticlebioRxiv : the preprint server for biology2025

GenomeOcean: An Efficient Genome Foundation Model Trained on Large-Scale Metagenomic Assemblies.

Zhihan Zhou, Robert Riley, Satria Kautsar, Weimin Wu, Rob Egan, Steven Hofmeyr, Shira Goldhaber-Gordon, Mutian Yu, Harrison Ho, Fengchen Liu and 5 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. 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

15 authors.

Zhihan ZhouNorthwestern University, Evanston, IL, USA.ORCID 0000-0002-1473-9071
Robert RileyLawrence Berkeley National Laboratory, Berkeley, CA, USA.ORCID 0000-0003-0224-0975
Satria KautsarLawrence Berkeley National Laboratory, Berkeley, CA, USA.ORCID 0000-0002-9815-511X
Weimin WuNorthwestern University, Evanston, IL, USA.ORCID 0009-0004-5901-0219
Rob EganLawrence Berkeley National Laboratory, Berkeley, CA, USA.
Steven HofmeyrLawrence Berkeley National Laboratory, Berkeley, CA, USA.
Shira Goldhaber-GordonJohn Hopkins University, Baltimore, MD, USA.
Mutian YuNorthwestern University, Evanston, IL, USA.
Harrison HoLawrence Berkeley National Laboratory, Berkeley, CA, USA.ORCID 0000-0002-8937-8266
Fengchen LiuLawrence Berkeley National Laboratory, Berkeley, CA, USA.
Feng ChenIllumina Inc., Foster City, CA, USA.
Rachael Morgan-KissMiami University, Oxford, Ohio, USA.ORCID 0000-0003-4783-5358
Lizhen ShiNorthwestern University, Evanston, IL, USA.
Han LiuNorthwestern University, Evanston, IL, USA.
Zhong WangLawrence Berkeley National Laboratory, Berkeley, CA, USA.ORCID 0000-0002-6307-0458

Funding

Developing Novel Deep-Learning Based Methods for Deciphering Non-Coding Gene Regulatory CodeR01LM013722 · NLM · STATE UNIVERSITY NEW YORK STONY BROOK · PI RAMANA V DAVULURI · 2021 to 2026
$2.0M
NLM NIH HHS R01 LM013722
6 · The paper itself

Abstract

Genome foundation models hold transformative potential for precision medicine, drug discovery, and understanding complex biological systems. However, existing models are often inefficient, constrained by suboptimal tokenization and architectural design, and biased toward reference genomes, limiting their representation of low-abundance, uncultured microbes in the rare biosphere. To address these challenges, we developed

Indexed as

Genome Foundation ModelMetagenomics

Identifiers

PMID39975405
PMCPMC11838515

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.