Showing results (691-700 of 944) with videos related to
Sort By:
Pageof 95
Patterns (New York, N.Y.)|August 22, 2026
Deep learning enabled prediction of nuclear lamina-associated chromatinPriyojit Das, Jeannie T LeePatterns (New York, N.Y.)|August 22, 2026
Revealing hidden material composition via physics-induced deep learning for precision clinical diagnosisMengyang Liu, Jiachen Liu, Qingyang Liu, et al.Patterns (New York, N.Y.)|August 22, 2026
From data to value in multimodal learningChunlei Tang, Xing Jia, Yun XiongPatterns (New York, N.Y.)|August 22, 2026
Density-based longitudinal neuron tracking in high-density electrophysiological recordingsYue Huang, Hanbo Wang, Jiaming Cao, et al.Patterns (New York, N.Y.)|October 17, 2026
Q-SID: Detecting collusion groups from exam question scores with error quantificationGuanao Yan, Jingyi Jessica Li, Mark D BigginPatterns (New York, N.Y.)|October 17, 2026
Disentangling batch effects and biological signals across conditions in single-cell transcriptomeShunta Sakaguchi, Masato Tsutsumi, Kentaro Nishi, et al.Patterns (New York, N.Y.)|October 17, 2026
Generalization of biological foundation models requires prospective validation across contextsLucas A Gillenwater, Casey S GreenePatterns (New York, N.Y.)|September 16, 2026
Erratum: Navigating COP16's digital sequence information outcomes: What researchers need to do in practiceMelania Muñoz-García, , Amber Hartman ScholzPatterns (New York, N.Y.)|September 16, 2026
Efficient attention mechanisms for large language modelsYutao Sun, Zhenyu Li, Yike Zhang, et al.Patterns (New York, N.Y.)|September 16, 2026
EmmaEmb: A quantitative framework for analyzing embedding spaces in molecular biologyPia Francesca Rissom, Vít Škrhák, Paulo Yanez Sarmiento, et al.Pageof 95