Author

Hongmin Li

Institute of Science TokyoORCID 0000-0003-0228-0600Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Research Focus:sequence-to-function modeling· 2 Organism:bacteria (other)· 1 Research Focus:benchmarking· 1 Assay:CITE-seq· 1 Organism:human· 1 Assay:long-read sequencing· 1 Assay:metagenomics· 1

Frequent Co-Authors

Bian BianiD· 1 shared Goro TeraiiD· 1 shared Jiuzhou Zhong· 1 shared Kiyoshi AsaiiD· 1 shared Takumi OtagakiiD· 1 shared Yiming Zhang· 1 shared Yutaka Saito· 1 shared

Activity by Year

2025-2026 · peak 1/yr

Co-Author Collaboration Network

7 top collaborators · 9 mutual co-author links

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1 shared paperBioinformatics

Bian BianORCID

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2 papers

Recent·Most cited

BioinformaticsBioinformaticsPeer-reviewedAug 31, 2026

Gradient-based Optimization for mRNA Sequence Design

Hongmin Li, Goro Terai, Takumi Otagaki, Kiyoshi Asai

The paper introduces ID3, a framework for optimizing mRNA sequences using gradient-based methods, balancing multiple properties while preserving amino acid sequences.

DOI 10.1093/bioinformatics/btag667

metagenomicsdeep learning

𝕏 PostBluesky

BioinformaticsbioRxivPreprintDec 24, 2025

Large generative mRNA language foundation model for efficient coding sequence generation and design with mRNA-GPT

Bian Bian, Yiming Zhang, Hongmin Li…·Yutaka Saito

The paper introduces mRNA-GPT, a generative mRNA language model trained on sequences from bacteria, eukaryotes, and archaea, showing improved translation efficiency and stability in mRNA design.

1 citation

bacteria (other)human

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