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[1S-15-1] Gene functions in natura - approaches using machine learning and mutants to analyze phenome, transcriptome and ecological data in naturally fluctuating environments

〇Kentaro K. Shimizu1,2, Reinhold Stockenhuber1, Reiko Akiyama1, Nicolas Tissot3, Michele Wyler1, Alex Widmer4, Roman Ulm3, Jianqiang Sun5, Jun Sese5, Rie Shimizu-Inatsugi1, Toshiaki Tameshige2, Aya Tonouchi6, Natsumaro Kutsuna6, Kenta Tanaka7, Hiroshi Kudoh8, Yasuhiro Sato9, Atsushi Nagano J9, Eri Yamasaki1, Roman Briskine1, Tomoaki Nishiyama10, Tomonori Kume11, Kaya Usun Shimizu12, Iku Asano13, Takao Itioka13, Shin Nagai14, Misako Yamazaki1 (1.Dept Evol Biol Env Studies, Univ Zurich, 2.Kihara Inst Biol Sci, Yokohama City Univ, 3.Dept Bot Plant Biol, Univ Geneva, 4.ETHZ, 5.Artificial Intelligence Research Center, AIST, 6.LPixel, Inc., 7.Sugadaira, Tsukuba Univ, 8.Center Ecol Res, Kyoto Univ, 9.Fac Agr, Ryukoku Univ, 10.Adv Sci Res Cen, Kanazawa Univ, 11.Fac Agr, Kyushu Univ, 12.Fac Life Env Sci, Shimane Univ, 13.Grad Sch Human Env Studies, Kyoto Univ, 14.JAMSTEC)

Arabidopsis, machine learning, ecology

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