Ke Ni

Ke Ni

he/him

Applied Scientist at Amazon. The Joint CMU–Pitt Ph.D. Program in Computational Biology (CPCB). I work on multimodal models, LLMs, computer vision, and AI for science.

OTMODE: an optimal transport theory-based framework for identifying differential features in single-cell multi-omics data

Published in 2025, 2025

Co-authored paper.

Recommended citation: Huidong Su, Caicai Zhang, Frank Qingyun Wang, Chun Hing She, Xinxin Chen, Xiao Dang, Yao Lei, Ke Ni, Zewei Xiong, Danqing Yin, Xingtian Yang, Hong Feng, Philip H Li, Wanling Yang. (2025). OTMODE: an optimal transport theory-based framework for identifying differential features in single-cell multi-omics data.