Jimin Tan, PhD

Institute for Systems Genetics

Research Assistant Professor, Department of Medicine

Keywords
Regulatory Genomics, Machine Learning, Generative Design, 3D Genome
Summary

We build machine learning models to understand how gene expression is regulated and to guide the design of regulatory DNA. Across thousands of transcription factors and millions of cis-regulatory elements, our goal is to capture, distill, and interpret the regulatory grammar embedded in large-scale genomic data. We believe impactful models emerge from biology-grounded, thoughtful engineering paired with scalable computation. Using these approaches, we have identified new mechanisms and potential therapeutic targets across multiple disease areas, including cancer and autoimmunity. Looking ahead, we aim to connect regulation across biological scales, from cells to tissues, organs, and individuals, to support precision medicine, enable therapeutic development, and advance our understanding of human biology.

Academic office

45-18 Ct Square W

5th Floor, 604

Long Island City, NY 11101

Lab Website
Tan Lab
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Positions

Research Assistant Professor, Department of Medicine at NYU Grossman School of Medicine

Graduate Education

PhD from New York University

Serologic Testing and Genomic Analysis of Autoimmune Immune-Mediated and Rheumatic Patients with COVID-19 (SAGA)

ELLIPSS: ELucidating the Landscape of Immunoendotypes in Psoriatic Skin and Synovium

See All Studies (2)

Cell-type-specific prediction of 3D chromatin organization enables high-throughput in silico genetic screening

Tan, Jimin; Shenker-Tauris, Nina; Rodriguez-Hernaez, Javier; Wang, Eric; Sakellaropoulos, Theodore; Boccalatte, Francesco; Thandapani, Palaniraja; Skok, Jane; Aifantis, Iannis; Fenyö, David; Xia, Bo; Tsirigos, Aristotelis

Nature biotechnology. 2023 Aug; 41(8):1140-1150

Characterization of tumour heterogeneity through segmentation-free representation learning on multiplexed imaging data

Tan, Jimin; Le, Hortense; Deng, Jiehui; Liu, Yingzhuo; Hao, Yuan; Hollenberg, Michelle; Liu, Wenke; Wang, Joshua M; Xia, Bo; Ramaswami, Sitharam; Mezzano, Valeria; Loomis, Cynthia; Murrell, Nina; Moreira, Andre L; Cho, Kyunghyun; Pass, Harvey I; Wong, Kwok-Kin; Ban, Yi; Neel, Benjamin G; Tsirigos, Aristotelis; Fenyö, David

Nature biomedical engineering. 2025 Mar; 9(3):405-419

A foundation model of transcription across human cell types

Fu, Xi; Mo, Shentong; Buendia, Alejandro; Laurent, Anouchka P; Shao, Anqi; Alvarez-Torres, Maria Del Mar; Yu, Tianji; Tan, Jimin; Su, Jiayu; Sagatelian, Romella; Ferrando, Adolfo A; Ciccia, Alberto; Lan, Yanyan; Owens, David M; Palomero, Teresa; Xing, Eric P; Rabadan, Raul

Nature. 2025 Jan; 637(8047):965-973

3D chromatin hubs as regulatory units of identity and survival in human acute leukemia

Gambi, Giovanni; Boccalatte, Francesco; Rodriguez Hernaez, Javier; Lin, Ziyan; Nadorp, Bettina; Polyzos, Alexander; Tan, Jimin; Avrampou, Kleopatra; Inghirami, Giorgio; Kentsis, Alex; Apostolou, Effie; Aifantis, Iannis; Tsirigos, Aristotelis

Molecular cell. 2025 Jan 02; 85(1):42-60.e7

Deep learning integrates histopathology and proteogenomics at a pan-cancer level

Wang, Joshua M; Hong, Runyu; Demicco, Elizabeth G; Tan, Jimin; Lazcano, Rossana; Moreira, Andre L; Li, Yize; Calinawan, Anna; Razavian, Narges; Schraink, Tobias; Gillette, Michael A; Omenn, Gilbert S; An, Eunkyung; Rodriguez, Henry; Tsirigos, Aristotelis; Ruggles, Kelly V; Ding, Li; Robles, Ana I; Mani, D R; Rodland, Karin D; Lazar, Alexander J; Liu, Wenke; Fenyö, David; ,

Cell reports. Medicine. 2023 Sep 19; 4(9):101173

Proteogenomic insights suggest druggable pathways in endometrial carcinoma

Dou, Yongchao; Katsnelson, Lizabeth; Gritsenko, Marina A; Hu, Yingwei; Reva, Boris; Hong, Runyu; Wang, Yi-Ting; Kolodziejczak, Iga; Lu, Rita Jui-Hsien; Tsai, Chia-Feng; Bu, Wen; Liu, Wenke; Guo, Xiaofang; An, Eunkyung; Arend, Rebecca C; Bavarva, Jasmin; Chen, Lijun; Chu, Rosalie K; Czekański, Andrzej; Davoli, Teresa; Demicco, Elizabeth G; DeLair, Deborah; Devereaux, Kelly; Dhanasekaran, Saravana M; Dottino, Peter; Dover, Bailee; Fillmore, Thomas L; Foxall, McKenzie; Hermann, Catherine E; Hiltke, Tara; Hostetter, Galen; Jędryka, Marcin; Jewell, Scott D; Johnson, Isabelle; Kahn, Andrea G; Ku, Amy T; Kumar-Sinha, Chandan; Kurzawa, Paweł; Lazar, Alexander J; Lazcano, Rossana; Lei, Jonathan T; Li, Yi; Liao, Yuxing; Lih, Tung-Shing M; Lin, Tai-Tu; Martignetti, John A; Masand, Ramya P; Matkowski, Rafał; McKerrow, Wilson; Mesri, Mehdi; Monroe, Matthew E; Moon, Jamie; Moore, Ronald J; Nestor, Michael D; Newton, Chelsea; Omelchenko, Tatiana; Omenn, Gilbert S; Payne, Samuel H; Petyuk, Vladislav A; Robles, Ana I; Rodriguez, Henry; Ruggles, Kelly V; Rykunov, Dmitry; Savage, Sara R; Schepmoes, Athena A; Shi, Tujin; Shi, Zhiao; Tan, Jimin; Taylor, Mason; Thiagarajan, Mathangi; Wang, Joshua M; Weitz, Karl K; Wen, Bo; Williams, C M; Wu, Yige; Wyczalkowski, Matthew A; Yi, Xinpei; Zhang, Xu; Zhao, Rui; Mutch, David; Chinnaiyan, Arul M; Smith, Richard D; Nesvizhskii, Alexey I; Wang, Pei; Wiznerowicz, Maciej; Ding, Li; Mani, D R; Zhang, Hui; Anderson, Matthew L; Rodland, Karin D; Zhang, Bing; Liu, Tao; Fenyö, David; ,

Cancer cell. 2023 Sep 11; 41(9):1586-1605.e15

Prediction of Total Knee Replacement and Diagnosis of Osteoarthritis by Using Deep Learning on Knee Radiographs: Data from the Osteoarthritis Initiative

Leung, Kevin; Zhang, Bofei; Tan, Jimin; Shen, Yiqiu; Geras, Krzysztof J; Babb, James S; Cho, Kyunghyun; Chang, Gregory; Deniz, Cem M

Radiology. 2020 Jun 23; 192091

Semi-supervised learning for predicting total knee replacement with unsupervised data augmentation

Tan, Jimin; Zhang, Bofei; Cho, Kyunghyun; Chang, Gregory; Deniz, Cem M.

Proceedings of SPIE (The International Society for Optical Engineering). 2020; 11314(00):113140P-113140P

See All Publications (10)