Marks, Debora S.

Human MicroRNA Targets

John, B., Enright, A. J., Aravin, A., Tuschl, T., Sander, C., & Marks, D. S. (2004) · PLoS Biology

MicroRNA in disease regulationPlant Molecular Biology Research

Cited by 3,796

MicroRNA targets in Drosophila

Enright, A. J., John, B., Gaul, U., Tuschl, T., Sander, C., & Marks, D. S. (2003) · Genome Biology

MicroRNA in disease regulationPlant Molecular Biology Research

Cited by 3,597

Scientific discovery in the age of artificial intelligence.

Wang, H., Fu, T., Du, Y., Gao, W., Huang, K., Liu, Z., Chandak, P., Liu, S., Van Katwyk, P., Deac, A., et al. (2023) · Nature

Artificial Intelligence ApplicationsMachine Learning in Materials Science

Cited by 1,823

Direct-coupling analysis of residue coevolution captures native contacts across many protein families

Morcos, F., Pagnani, A., Lunt, B., Bertolino, A., Marks, D. S., Sander, C., Zecchina, R., Onuchic, J. N., Hwa, T., & Weigt, M. (2011) · Proceedings of the National Academy of Sciences

Protein Structure and DynamicsMachine Learning in Bioinformatics

Cited by 1,434

Protein 3D Structure Computed from Evolutionary Sequence Variation

Marks, D. S., Colwell, L. J., Sheridan, R., Hopf, T. A., Pagnani, A., Zecchina, R., & Sander, C. (2011) · PLoS ONE

Protein Structure and DynamicsEnzyme Structure and Function

Cited by 1,144

Disease variant prediction with deep generative models of evolutionary data

Frazer, J., Notin, P., Dias, M., Gomez, A., Min, J. K., Brock, K., Gal, Y., & Marks, D. S. (2021) · Nature

Genomics and Rare DiseasesGenetic Associations and Epidemiology

Cited by 791

Mutation effects predicted from sequence co-variation

Hopf, T. A., Ingraham, J. B., Poelwijk, F. J., Schärfe, C. P. I., Springer, M., Sander, C., & Marks, D. S. (2017) · Nature Biotechnology

Genomics and Rare DiseasesEvolution and Genetic Dynamics

Cited by 775

Protein structure prediction from sequence variation

Marks, D. S., Hopf, T. A., & Sander, C. (2012) · Nature Biotechnology

Protein Structure and DynamicsEnzyme Structure and Function

Cited by 622

Deep generative models of genetic variation capture the effects of mutations

Riesselman, A. J., Ingraham, J. B., & Marks, D. S. (2018) · Nature Methods

Genomics and Phylogenetic StudiesEvolution and Genetic Dynamics

Cited by 601

Transfection of small RNAs globally perturbs gene regulation by endogenous microRNAs

Khan, A. A., Betel, D., Miller, M. L., Sander, C., Leslie, C. S., & Marks, D. S. (2009) · Nature Biotechnology

MicroRNA in disease regulationRNA Interference and Gene Delivery

Cited by 534

Sequence co-evolution gives 3D contacts and structures of protein complexes

Hopf, T. A., Schärfe, C. P. I., Rodrigues, J. P. G. L. M., Green, A. G., Kohlbacher, O., Sander, C., Bonvin, A. M. J. J., & Marks, D. S. (2014) · eLife

Protein Structure and DynamicsMachine Learning in Bioinformatics

Cited by 515

MicroRNA profiling of the murine hematopoietic system

Monticelli, S., Ansel, K. M., Xiao, C., Socci, N. D., Krichevsky, A. M., Thai, T.-H., Rajewsky, N., Marks, D. S., Sander, C., Rajewsky, K., et al. (2005) · Genome Biology

MicroRNA in disease regulationPlant Molecular Biology Research

Cited by 436

MicroRNA control of protein expression noise

Schmiedel, J. M., Klemm, S. L., Zheng, Y., Sahay, A., Blüthgen, N., Marks, D. S., & van Oudenaarden, A. (2015) · Science

MicroRNA in disease regulationGene Regulatory Network Analysis

Cited by 395

A deep learning algorithm to predict risk of pancreatic cancer from disease trajectories

Placido, D., Yuan, B., Hjaltelin, J. X., Zheng, C., Haue, A. D., Chmura, P. J., Yuan, C., Kim, J., Umeton, R., Antell, G., et al. (2023) · Nature Medicine

Machine Learning in HealthcarePancreatic and Hepatic Oncology Research

Cited by 349

Protein design and variant prediction using autoregressive generative models

Shin, J.-E., Riesselman, A. J., Kollasch, A. W., McMahon, C., Simon, E., Sander, C., Manglik, A., Kruse, A. C., & Marks, D. S. (2021) · Nature Communications

Protein Structure and DynamicsMachine Learning in Bioinformatics

Cited by 320

scPerturb: harmonized single-cell perturbation data

Peidli, S., Green, T. D., Shen, C., Gross, T., Min, J., Garda, S., Yuan, B., Schumacher, L. J., Taylor-King, J. P., Marks, D. S., et al. (2024) · Nature Methods

Single-cell and spatial transcriptomicsGene Regulatory Network Analysis

Cited by 194

3D RNA and Functional Interactions from Evolutionary Couplings

Weinreb, C., Riesselman, A. J., Ingraham, J. B., Gross, T., Sander, C., & Marks, D. S. (2016) · Cell

RNA and protein synthesis mechanismsProtein Structure and Dynamics

Cited by 159

Tranception: protein fitness prediction with autoregressive transformers and inference-time retrieval

Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A., Marks, D. S., & Gal, Y. (2022) · arXiv

Machine Learning in BioinformaticsProtein Structure and Dynamics

Cited by 118

Large-scale discovery of protein interactions at residue resolution using co-evolution calculated from genomic sequences

Green, A. G., Elhabashy, H., Brock, K. P., Maddamsetti, R., Kohlbacher, O., & Marks, D. S. (2021) · Nature Communications

Protein Structure and DynamicsMachine Learning in Bioinformatics

Cited by 114

PDBe-KB: collaboratively defining the biological context of structural data

PDBe-KB consortium, Varadi, M., Anyango, S., Armstrong, D., Berrisford, J., Choudhary, P., Deshpande, M., Nadzirin, N., Nair, S. S., Pravda, L., et al. (2022) · Nucleic Acids Research

Enzyme Structure and FunctionProtein Structure and Dynamics

Cited by 82

MaveDB 2024: a curated community database with over seven million variant effects from multiplexed functional assays

Rubin, A. F., Stone, J., Bianchi, A. H., Capodanno, B. J., Da, E. Y., Dias, M., Esposito, D., Frazer, J., Fu, Y., Grindstaff, S. B., et al. (2025) · Genome Biology

Genomics and Rare DiseasesCRISPR and Genetic Engineering

Cited by 80

Continuous evolution of user-defined genes at 1-million-times the genomic mutation rate

Rix, G., Williams, R. L., Spinner, H., Hu, V. J., Marks, D. S., & Liu, C. C. (2023) · Science

Evolution and Genetic DynamicsMicrobial Metabolic Engineering and Bioproduction

Cited by 71

Continuous evolution of user-defined genes at 1 million times the genomic mutation rate

Rix, G., Williams, R. L., Hu, V. J., Spinner, A., Pisera, A. (., Marks, D. S., & Liu, C. C. (2024) · Science

Evolution and Genetic DynamicsMicrobial Metabolic Engineering and Bioproduction

Cited by 71

Evolutionary-Scale Enzymology Enables Biochemical Constant Prediction Across a Multi-Peaked Catalytic Landscape

Muir, D. F., Asper, G. P. R., Notin, P., Posner, J. A., Marks, D. S., Keiser, M. J., & Pinney, M. M. (2024) · Science

Protein Structure and DynamicsEnzyme Catalysis and Immobilization

Cited by 42