Jagadeesh, Karthik A.

Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA

Liu, M. C., Oxnard, G. R., Klein, E. A., Swanton, C., Seiden, M. V., Liu, M. C., Oxnard, G. R., Klein, E. A., Smith, D., Richards, D., et al. (2020) · Annals of Oncology

Cancer Genomics and DiagnosticsEpigenetics and DNA Methylation

Cited by 1,495

Single-nucleus and spatial transcriptome profiling of pancreatic cancer identifies multicellular dynamics associated with neoadjuvant treatment

Hwang, W. L., Jagadeesh, K. A., Guo, J. A., Hoffman, H. I., Yadollahpour, P., Reeves, J. W., Mohan, R., Drokhlyansky, E., Van Wittenberghe, N., Ashenberg, O., et al. (2022) · Nature Genetics

Pancreatic and Hepatic Oncology ResearchSingle-cell and spatial transcriptomics

Cited by 426

Polygenic enrichment distinguishes disease associations of individual cells in single-cell RNA-seq data

Zhang, M. J., Hou, K., Dey, K. K., Sakaue, S., Jagadeesh, K. A., Weinand, K., Taychameekiatchai, A., Rao, P., Pisco, A. O., Zou, J., et al. (2022) · Nature Genetics

Single-cell and spatial transcriptomicsGenetic Associations and Epidemiology

Cited by 302

Identifying disease-critical cell types and cellular processes by integrating single-cell RNA-sequencing and human genetics

Jagadeesh, K. A., Dey, K. K., Montoro, D. T., Mohan, R., Gazal, S., Engreitz, J. M., Xavier, R. J., Price, A. L., & Regev, A. (2022) · Nature Genetics

Genetic Associations and EpidemiologySingle-cell and spatial transcriptomics

Cited by 239

Combining SNP-to-gene linking strategies to identify disease genes and assess disease omnigenicity

Gazal, S., Weissbrod, O., Hormozdiari, F., Dey, K. K., Nasser, J., Jagadeesh, K. A., Weiner, D. J., Shi, H., Fulco, C. P., O’Connor, L. J., et al. (2022) · Nature Genetics

Genetic Associations and EpidemiologyGenetic Mapping and Diversity in Plants and Animals

Cited by 181

Deriving genomic diagnoses without revealing patient genomes

Jagadeesh, K. A., Wu, D. J., Birgmeier, J. A., Boneh, D., & Bejerano, G. (2017) · Science

Cryptography and Data SecurityPrivacy-Preserving Technologies in Data

Cited by 134

AMELIE speeds Mendelian diagnosis by matching patient phenotype and genotype to primary literature

Birgmeier, J., Haeussler, M., Deisseroth, C. A., Steinberg, E. H., Jagadeesh, K. A., Ratner, A. J., Guturu, H., Wenger, A. M., Diekhans, M. E., Stenson, P. D., et al. (2020) · Science Translational Medicine

Genomics and Rare DiseasesBiomedical Text Mining and Ontologies

Cited by 118

Leveraging single-cell ATAC-seq and RNA-seq to identify disease-critical fetal and adult brain cell types

Kim, S. S., Truong, B., Jagadeesh, K., Dey, K. K., Shen, A. Z., Raychaudhuri, S., Kellis, M., & Price, A. L. (2024) · Nature Communications

Single-cell and spatial transcriptomicsGenetic Associations and Epidemiology

Cited by 56

Phrank measures phenotype sets similarity to greatly improve Mendelian diagnostic disease prioritization

Jagadeesh, K. A., Birgmeier, J., Guturu, H., Deisseroth, C. A., Wenger, A. M., Bernstein, J. A., & Bejerano, G. (2019) · Genetics in Medicine

Genomics and Rare DiseasesBiomedical Text Mining and Ontologies

Cited by 56

Mapping the dynamic genetic regulatory architecture of HLA genes at single-cell resolution

Kang, J. B., Shen, A. Z., Gurajala, S., Nathan, A., Rumker, L., Aguiar, V. R. C., Valencia, C., Lagattuta, K. A., Zhang, F., Jonsson, A. H., et al. (2023) · Nature Genetics

Genetic Associations and EpidemiologySingle-cell and spatial transcriptomics

Cited by 49