A Phylogenetic Approach to Genomic Language Modeling.

Carlos Albors, Jianan Canal Li, Gonzalo Benegas, Chengzhong Ye, Yun S Song

Abstract readPreprint

In one paragraph

Article in ArXiv, 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

5 authors.

Carlos AlborsDepartment of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720, USA.

Jianan Canal LiDepartment of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720, USA.

Gonzalo BenegasDepartment of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720, USA.

Chengzhong YeDepartment of Statistics, University of California, Berkeley, CA 94720, USA.

Yun S SongDepartment of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720, USA.

Funding

Robust and efficient statistical inference methods for genomicsR35GM134922 · NIGMS · UNIVERSITY OF CALIFORNIA BERKELEY · PI SONG, YUN S · 2020 to 2024

$2.0M

NIGMS NIH HHS R35 GM134922

6 · The paper itself

Abstract

Genomic language models (gLMs) have shown mostly modest success in identifying evolutionarily constrained elements in mammalian genomes. To address this issue, we introduce a novel framework for training gLMs that explicitly models nucleotide evolution on phylogenetic trees using multispecies whole-genome alignments. Our approach integrates an alignment into the loss function during training but does not require it for making predictions, thereby enhancing the model's applicability. We applied this framework to train PhyloGPN, a model that excels at predicting functionally disruptive variants from a single sequence alone and demonstrates strong transfer learning capabilities.

Identifiers

PMID40093357

PMCPMC11908359

What OpenQuestion holds

Textmetadata

LicenceCC BY-NC-ND

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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.