ArticlebioRxiv : the preprint server for biology2025
Pangenome-Informed Language Models for Synthetic Genome Sequence Generation.
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. Cited by 1 paper.
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1 citing paper in PubMed.
- Population health management through human phenotype ontology with policy for ecosystem improvement.Frontiers in artificial intelligence · 2025Article
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Authors and funding
7 authors.
Funding
Abstract
Language Models (LM) have been extensively utilized for learning DNA sequence patterns and generating synthetic sequences. In this paper, we present a novel approach for the generation of synthetic DNA data using pangenomes in combination with LM. We introduce three innovative pangenome-based tokenization schemes that enhance DNA sequence generation. Our experimental results demonstrate the superiority of pangenome-based tokenization over classical methods in generating high-utility synthetic DNA sequences, highlighting significant improvements in training efficiency and sequence quality.
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Registered trials
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