Song, Yun S.

The Simons Genome Diversity Project: 300 genomes from 142 diverse populations

Mallick, S., Li, H., Lipson, M., Mathieson, I., Gymrek, M., Racimo, F., Zhao, M., Chennagiri, N., Nordenfelt, S., Tandon, A., et al. (2016) · Nature

Forensic and Genetic ResearchPleistocene-Era Hominins and Archaeology

Cited by 1,584

Genotype and SNP calling from next-generation sequencing data

Nielsen, R., Paul, J. S., Albrechtsen, A., & Song, Y. S. (2011) · Nature Reviews Genetics

Genomics and Phylogenetic StudiesGenomics and Rare Diseases

Cited by 1,396

Evaluating Protein Transfer Learning with TAPE

Rao, R., Bhattacharya, N., Thomas, N., Duan, Y., Chen, X., Canny, J., Abbeel, P., & Song, Y. S. (2019) · bioRxiv (Cold Spring Harbor Laboratory)

Machine Learning in BioinformaticsProtein Structure and Dynamics

Cited by 719

DNA language models are powerful predictors of genome-wide variant effects

Benegas, G., Batra, S. S., & Song, Y. S. (2023) · Proceedings of the National Academy of Sciences

Genomics and Phylogenetic StudiesGenetic Associations and Epidemiology

Cited by 157

Constructing Minimal Ancestral Recombination Graphs

Song, Y. S., & Hein, J. (2005) · Journal of Computational Biology

Genomics and Phylogenetic StudiesGenetic diversity and population structure

Cited by 130

GPN-MSA: an alignment-based DNA language model for genome-wide variant effect prediction

Benegas, G., Albors, C., Aw, A. J., Ye, C., & Song, Y. S. (2023) · Nature Biotechnology

Genomics and Rare DiseasesGenomics and Phylogenetic Studies

Cited by 118

Model‐based detection and analysis of introgressed Neanderthal ancestry in modern humans

Steinrücken, M., Spence, J. P., Kamm, J. A., Wieczorek, E., & Song, Y. S. (2018) · Molecular Ecology

Pleistocene-Era Hominins and ArchaeologyForensic and Genetic Research

Cited by 73

Cross-protein transfer learning substantially improves disease variant prediction

Jagota, M., Ye, C., Albors, C., Rastogi, R., Koehl, A., Ioannidis, N., & Song, Y. S. (2022) · Genome biology

Genomics and Rare DiseasesGenetic Associations and Epidemiology

Cited by 68

Interpreting Potts and Transformer Protein Models Through the Lens of Simplified Attention

Bhattacharya, N., Thomas, N., Rao, R., Dauparas, J., Koo, P. K., Baker, D., Song, Y. S., & Ovchinnikov, S. (2021) · Biocomputing 2022

Machine Learning in BioinformaticsProtein Structure and Dynamics

Cited by 20

CherryML: Scalable Maximum Likelihood Estimation of Phylogenetic Models

Prillo, S., Deng, Y., Boyeau, P., Li, X., Chen, P.-Y., & Song, Y. S. (2022) · Nature Methods

Genomics and Phylogenetic StudiesRNA and protein synthesis mechanisms

Cited by 13

CherryML: scalable maximum likelihood estimation of phylogenetic models

Prillo, S., Deng, Y., Boyeau, P., Li, X., Chen, P.-Y., & Song, Y. S. (2023) · Nature Methods

Genomics and Phylogenetic StudiesRNA and protein synthesis mechanisms

Cited by 13

Learning antibody sequence constraints from allelic inclusion

Jagota, M., Hsu, C., Mazumder, T., Sung, K., DeWitt, W. S., Listgarten, J., Matsen IV, F. A., Ye, C. J., & Song, Y. S. (2025) · Cell Systems

Monoclonal and Polyclonal Antibodies ResearchT-cell and B-cell Immunology

Cited by 2

Learning antibody sequence constraints from allelic inclusion

Jagota, M., Hsu, C., Mazumder, T., Sung, K., DeWitt, W. S., Listgarten, J., Matsen, F. A., Ye, C. J., & Song, Y. S. (2024) · Cell Systems

Monoclonal and Polyclonal Antibodies ResearchT-cell and B-cell Immunology

Cited by 2