*Japanese*
Evolutionary computation, Bioinformatics(Inference of biochemical networks)
Inference of Genetic Networks from Steady-state and\\ Pseudo Time-series of Single-cell Gene Expression Data using Modified Random Forests , S.Kimura, H.Kitajima, M.Tokuhisa, M.Okada , Proc. of the 2023 IEEE Symposium Series on Computational Intelligence , vol.1 (p.1579 - 1586) , 2023 , There is Review , The Multiple Authorship , English
Inference of Genetic Networks using Random Forests: Performance Improvement using a New Variable Importance Measure , S.Kimura, Y.Takeda, M.Tokuhisa, M.Okada , Chem-Bio Informatics Journal , vol.22 (p.88 - 109) , 2022 , There is Review , The Multiple Authorship , English
Inference of Genetic Networks using Random Forests: A Quantitative Weighting Method for Gene Expression Data , S.Kimura, K.Sota, M.Tokuhisa , Proc. of the 2022 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology , vol.1 (p.123 - 130) , 2022 , The Multiple Authorship , English
Feature Selection using Modified Null Importance , S.Kimura, D.Oda, M.Tokuhisa , Proc. of the 2021 IEEE Symposium Series on Computational Intelligence , vol.1 (p.1 - 7) , 2021 , The Multiple Authorship , English
Detection of Weak Relevant Variables using Random Forests , S.Kimura and M.Tokuhisa , Proc. of the SICE Annual Conference 2020 (p.838 - 845) , 2020 , There is Review , The Multiple Authorship , English
Updated on 2024/04/16