Jae H. Sul, Ph.D.
Affiliations: | 2013 | Computer Science | University of California, Los Angeles, Los Angeles, CA |
Area:
bioinformatics; computational biology; machine learningGoogle:
"Jae Sul"Parents
Sign in to add mentor| Eleazar Eskin | grad student | 2013 | UCLA | |
| (Design of efficient and statistically powerful approaches for human genetics.) | ||||
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Publications
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| Jung S, Lee CH, Sul JH, et al. (2023) Building an optimal predictive model for imputing tissue-specific gene expression by combining genotype and whole-blood transcriptome data. Hgg Advances. 4: 100223 |
| Kurki MI, Karjalainen J, Palta P, et al. (2023) FinnGen provides genetic insights from a well-phenotyped isolated population. Nature. 613: 508-518 |
| Johnson R, Ding Y, Venkateswaran V, et al. (2022) Author Correction: Leveraging genomic diversity for discovery in an electronic health record linked biobank: the UCLA ATLAS Community Health Initiative. Genome Medicine. 14: 128 |
| Johnson R, Ding Y, Venkateswaran V, et al. (2022) Leveraging genomic diversity for discovery in an electronic health record linked biobank: the UCLA ATLAS Community Health Initiative. Genome Medicine. 14: 104 |
| Spendlove SJ, Bondhus L, Lluri G, et al. (2022) Polygenic risk scores of endo-phenotypes identify the effect of genetic background in congenital heart disease. Hgg Advances. 3: 100112 |
| Zhan L, Li J, Jew B, et al. (2021) Rare variants in the endocytic pathway are associated with Alzheimer's disease, its related phenotypes, and functional consequences. Plos Genetics. 17: e1009772 |
| Li J, Kong N, Han B, et al. (2021) Rare variants regulate expression of nearby individual genes in multiple tissues. Plos Genetics. 17: e1009596 |
| Jew B, Sul JH. (2021) Variant calling and quality control of large-scale human genome sequencing data. Emerging Topics in Life Sciences. 3: 399-409 |
| Jew B, Sul JH. (2021) Variant calling and quality control of large-scale human genome sequencing data. Emerging Topics in Life Sciences. 3: 399-409 |
| Gorla A, Jew B, Zhang L, et al. (2021) xGAP: A python based efficient, modular, extensible and fault tolerant genomic analysis pipeline for variant discovery. Bioinformatics (Oxford, England) |