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Your search keyword '"Kobayashi, Masaki"' showing total 32 results
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32 results on '"Kobayashi, Masaki"'

3. Conclusion

Author

Farr, Fiona, primary, Heron, Marion, additional, Kobayashi, Emi, additional, Kobayashi, Masaki, additional, Louw, Stephen, additional, Pelletier, Caroline, additional, and Riordan, Elaine, additional

Published
2021
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30. Tumor Specificity and the Type of Cell Death Induced by Heterocycles.

Author

Sakagami, Hiroshi, Kobayashi, Masaki, Ishihara, Mariko, Kikuchi, Hirotaka, Nakamura, Yukio, Kawase, Masami, and Motohashi, Noboru

Abstract

Considering the presence of multiple types of cell death induced by chemicals, it is important to determine a definitive strategy for the exploration of new highly tumor-selective compounds. The screening of highly selective compounds should be performed before the identification of the type of cell death (either apoptosis, autophagy, or necrosis) and the cell death induction mechanism. The tumor specificities of heterocyclic compounds and the type of cell death induced by them are summarized. [ABSTRACT FROM AUTHOR]

Published
2008
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31. Changes in Polyamine Levels During Cell Death Induced by Heterocycles.

Author

Kobayashi, Masaki, Sakagami, Hiroshi, Kawase, Masami, and Motohashi, Noboru

Abstract

Natural polyamines (putrescine, spermidine, spermine) are aliphatic amines containing 2 or more amino groups, and play an important role in regulating cell growth and differentiation. Depletion of polyamine in cells has been known to inhibit cell proliferation or induce cell death. This review summarizes changes in polyamine levels during cell death induced by the selective properties of various heterocycles. [ABSTRACT FROM AUTHOR]

Published
2008
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32. RWS (Random Walk Splitting): A Random Walk Based Discretization of Continuous Attributes.

Author

Goos, G., Hartmanis, J., van Leeuwen, J., Mizoguchi, Riichiro, Slaney, John, Carbonell, Jaime G., Siekmann, Jörg, Hanaoka, Masaaki, Kobayashi, Masaki, and Yamazaki, Haruaki

Abstract

The discretization of continuous attributes in a given training set is an important issue, which significantly affects the performance of decision trees. This paper proposes a method to discretize the continuous attributes based on a random walk modeled statistical test. In this method, the algorithm tries to find the point which divides the training set T into two groups T1 and T2 such that T = T1 ∪ T2 with possibly many instances from a majority class included in T1. In other words, the algorithm detects the splitting point, which gives the maximum discrepancy between the two empirical distributions, the majority class and the rest. The algorithm recursively executes this procedure until some statistical criterion is satisfied. Further, we report the effectiveness of the algorithm over ChiMerge and MDLPC based on an experiment with UCI repository. [ABSTRACT FROM AUTHOR]

Published
2000
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