Avsec, Žiga

Accurate proteome-wide missense variant effect prediction with AlphaMissense

Cheng, J., Novati, G., Pan, J., Bycroft, C., Žemgulytė, A., Applebaum, T., Pritzel, A., Wong, L. H., Zielinski, M., Sargeant, T., et al. (2023) · Science

Genomics and Rare DiseasesGenetic Associations and Epidemiology

Cited by 2,163

Effective gene expression prediction from sequence by integrating long-range interactions

Avsec, Ž., Agarwal, V., Visentin, D., Ledsam, J. R., Grabska-Barwinska, A., Taylor, K. R., Assael, Y., Jumper, J., Kohli, P., & Kelley, D. R. (2021) · Nature Methods

Genomics and Chromatin DynamicsGenetic Associations and Epidemiology

Cited by 1,326

Base-resolution models of transcription-factor binding reveal soft motif syntax

Avsec, Ž., Weilert, M., Shrikumar, A., Krueger, S., Alexandari, A., Dalal, K., Fropf, R., McAnany, C., Gagneur, J., Kundaje, A., et al. (2021) · Nature Genetics

Genomics and Chromatin DynamicsGene Regulatory Network Analysis

Cited by 610

Mapping single-cell data to reference atlases by transfer learning

Lotfollahi, M., Naghipourfar, M., Luecken, M. D., Khajavi, M., Büttner, M., Wagenstetter, M., Avsec, Ž., Gayoso, A., Yosef, N., Interlandi, M., et al. (2022) · Nature Biotechnology

Single-cell and spatial transcriptomicsGene Regulatory Network Analysis

Cited by 602

MMSplice: modular modeling improves the predictions of genetic variant effects on splicing

Cheng, J., Nguyen, T. Y. D., Cygan, K. J., Çelik, M. H., Fairbrother, W. G., Avsec, Ž., & Gagneur, J. (2019) · Genome Biology

RNA Research and SplicingGenomics and Rare Diseases

Cited by 235

Technical Note on Transcription Factor Motif Discovery from Importance Scores (TF-MoDISco) version 0.5.6.5

Shrikumar, A., Tian, K., Avsec, Ž., Shcherbina, A., Banerjee, A., Sharmin, M., Nair, S., & Kundaje, A. (2018) · arXiv

Genomics and Chromatin DynamicsGenomics and Phylogenetic Studies

Cited by 229

An encyclopedia of enhancer-gene regulatory interactions in the human genome

Gschwind, A. R., Mualim, K. S., Karbalayghareh, A., Sheth, M. U., Dey, K. K., Jagoda, E., Nurtdinov, R. N., Xi, W., Tan, A. S., Jones, H., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory)

Genomics and Chromatin DynamicsGenetic Associations and Epidemiology

Cited by 119

Cis -regulatory elements explain most of the mRNA stability variation across genes in yeast

Cheng, J., Maier, K. C., Avsec, Ž., Rus, P., & Gagneur, J. (2017) · RNA

RNA Research and SplicingRNA and protein synthesis mechanisms

Cited by 79

Predicting mean ribosome load for 5’UTR of any length using deep learning

Karollus, A., Avsec, Ž., & Gagneur, J. (2021) · PLOS Computational Biology

RNA and protein synthesis mechanismsRNA Research and Splicing

Cited by 57

Predicting Mean Ribosome Load for 5’UTR of any length using Deep Learning

Karollus, A., Avsec, Ž., & Gagneur, J. (2020) · PLoS Computational Biology

RNA and protein synthesis mechanismsRNA Research and Splicing

Cited by 57