[4P-0196] A machine learning approach for morphological analysis of non-essential gene mutants of Ubiquitin-Proteasome pathway

〇Godai Suzuki¹, Yutaka Saito¹, Motoaki Seki², Kentaro Kakoi², Mikiko Negishi², Hiroyuki Aburatani³, Daniel Evans Yamamoto^2,4,5, Christian R Landry^6,7,8,9,10, Nozomu Yachie^2,4,5,11,12, Totai Mitsuyama¹ (1.AIRC, AIST, 2.Synthetic Biol. Div., RCAST, Univ. of Tokyo, 3.Genome Sci. Div., RCAST, Univ. of Tokyo, 4.IAB, Keio Univ., 5.Systems Biol. Program, Grad. Sch. of Media and Governance, Keio Univ., 6.IBIS, Univ. Laval, 7.Dept. of Biochem., Microbiol. and Bioinfo., Faculty of Sci. and Engineering, Univ. Laval, 8.PROTEO, Univ. Laval, 9.CRDM, Univ. Laval, 10.Dept. of Biol., Faculty of Sci. and Engineering, Univ. Laval, 11.Dept. of Biol. Sci., Sch. of Sci., Univ. of Tokyo, 12.PRESTO, JST)

Ubiquitin-Proteasome, Machine learning, Cell morphology, Paralog