Universal short read adapters with variable length non-random unique molecular identifiers
Patent covering the general usage of variable length UMI barcodes for sequencing error suppression
Patent covering the general usage of variable length UMI barcodes for sequencing error suppression
Patent covering the generation of highly error tolerant variable length UMI barcodes
Published in RNA, 2020
Biologically-inspired featurization of RNA transcripts enables state-of-the-art modeling of RNA subcellular localization
Recommended citation: Wu, K.E., Parker, K.R., Fazal, F.M., Chang, H.Y. and Zou, J., 2020. RNA-GPS predicts high-resolution RNA subcellular localization and highlights the role of splicing. RNA, 26(7), pp.851-865. Vancouver https://rnajournal.cshlp.org/content/26/7/851.short
Published in Cell systems, 2020
Machine learning model for RNA localization allows hypothesis generation regarding behavior of SARS-CoV-2 transcriptome
Recommended citation: Wu, K.E., Fazal, F.M., Parker, K.R., Zou, J. and Chang, H.Y., 2020. RNA-GPS predicts SARS-CoV-2 RNA residency to host mitochondria and nucleolus. Cell systems, 11(1), pp.102-108. https://www.sciencedirect.com/science/article/pii/S2405471220302374
Published in Proceedings of the National Academy of Sciences, 2021
BABEL enables computational imputation of single-cell multi-omic profiles with only a single modality as input
Recommended citation: Wu, K.E., Yost, K.E., Chang, H.Y. and Zou, J., 2021. BABEL enables cross-modality translation between multiomic profiles at single-cell resolution. Proceedings of the National Academy of Sciences, 118(15), p.e2023070118. https://www.pnas.org/doi/abs/10.1073/pnas.2023070118
Published in bioRxiv, 2021
Large language modeling of T-cell receptor sequences
Recommended citation: Wu, K., Yost, K.E., Daniel, B., Belk, J.A., Xia, Y., Egawa, T., Satpathy, A., Chang, H.Y. and Zou, J., 2021. TCR-BERT: learning the grammar of T-cell receptors for flexible antigen-xbinding analyses. bioRxiv. https://www.biorxiv.org/content/10.1101/2021.11.18.469186v1.full.pdf
Published in Nature Communications, 2024
Generative diffusion model for protein structures; trigonometry is all you need!
Recommended citation: Wu, K. E., Yang, K. K., van den Berg, R., Alamdari, S., Zou, J. Y., Lu, A. X., & Amini, A. P. (2024). Protein structure generation via folding diffusion. Nature Communications, 15(1), 1059. https://www.nature.com/articles/s41467-024-45051-2
Published:
Spotlight talk at the ICML 2022 Workshop on Computational Biology. Webpage here, pdf here.
Published:
Invited talk on FoldingDiff
, see details here.
Published:
Invited talk on FoldingDiff
at the OpenBioML journal club, see recording on YouTube.
Graduate course, Stanford University, 2019
Course assistant for a course exploring cutting edge developments in the intersection of deep learning, genomics, and biomedicine. Led discussion/recitation sections and portions of lectures.