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Hyunghoon Cho

As seen in: Nature, Harvard Gazette, Genome Biology, Science Magazine, BioRxiv, Cell Press, Genome Research
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Articles

Memorization in large language models in medicine prevalence characteristics and implications

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By Anran Li, Mengmeng Du, Yu Yin, Yan Hu, Zihao Sun, Yihang Fu, Hyunjae R. Kim, Xuguang Ai, Qianqian Xie, RUI ZHU, Jimin Huang, Yifan Yang, Siru Liu, Yih-Chung Tham, Lucila Ohno-Machado, Hyunghoon Cho, Zhiyong Lu, Hua Xu, Qingyu Chen, Lingfei Qian, Erica Stutz
| Nature Verified
Abstract Large Language Models (LLMs) have demonstrated significant potential in medicine, with many studies adapting them through continued pretraining or fine-tuning on medical data. However, a key question remains: to what extent do LLMs memorize medical training data—that is, recall or regenerate content seen during continued pretraining or fine-tuning.
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Secure and federated genome-wide association studies for biobank-scale datasets - Nature Genetics

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By Hyunghoon Cho, Juan R. Troncoso-pastoriza, Jean-Pierre Hubaux
| Nature Verified
Abstract Sharing data across institutions for genome-wide association studies (GWAS) would enhance the discovery of genetic variation linked to health and disease1,2. However, existing data-sharing regulations limit the scope of such collaborations3. Although cryptographic tools for secure computation promise to enable collaborative analysis with formal privacy guarantees, existing approaches either are computationally impractical or do not implement current state-of-the-art methods4,5,6.
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TX-Phase: Secure Phasing of Private Genomes in a Trusted Execution Environment

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By Natnatee Dokmai, Kaiyuan Zhu, S. Cenk Sahinalp, Hyunghoon Cho
| BioRxiv
Abstract Genotype imputation servers enable researchers with limited resources to extract valuable insights from their data with enhanced accuracy and ease. However, the utility of these services is limited for those with sensitive study cohorts or those in restrictive regulatory environments due to data privacy concerns.
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