Author

Matthew Liu

Stanford UniversityPrimary ORCID profile (0000-0002-5004-9182 · 1 paper)Unclaimed ORCID profile · follow to get notified of new preprints

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Same name, identified by ORCID

Matthew LiuiD1 paper · Children's Hospital of Philadelphia

Biology Disciplines

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Bioengineering· 1 Evolutionary Biology· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Assay:DNA methylation· 1 Research Focus:drug response / drug discovery· 1 Research Focus:gene regulatory network inference· 1 Organism:human· 1 Organism:mouse· 1 Research Focus:phylogenetics / evolution· 1 Research Focus:protein design· 1

Frequent Co-Authors

Antoine KoehliD· 1 shared Brian HieiD· 1 shared Charu A Ramakrishnan· 1 shared Daniel ChangiD· 1 shared David F. SavageiD· 1 shared Divya NoriiD· 1 shared Garyk BrixiiD· 1 shared J. T. Xiong· 1 shared

Activity by Year

2026-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 21 mutual co-author links

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1 shared paperEvolutionary Biology

Antoine KoehlORCID

Mutual co-authors in cluster

Author profile →

2 papers

Recent·Most cited

Few-shot regulatory sequence design with a genomic language model

Brian Hie, Yukun A Hao, Matthew Liu, Divya Nori, Garyk Brixi, Thomas Lau, Charu A Ramakrishnan, Daniel Chang, Ryan Park, William E. Allen

This paper demonstrates that a genomic language model can generate functional regulatory DNA sequences using few examples, showing improved performance in cell-type-specific gene expression and neuronal labeling.

bioRxiv · Oct 30, 2026 · PreprintBioengineering

Deep models of protein evolution in time generate realistic evolutionary trajectories and functional proteins

Antoine Koehl, Sebastian Prillo, Matthew Liu, J. T. Xiong, Lillian Weng, David F. Savage, Yun S. Song

PEINT simulates realistic protein evolution by modeling sequence changes over time, generating functional proteins that retain enzymatic activity.

bioRxiv · Feb 20, 2026 · PreprintEvolutionary Biology