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Ichigaku Takigawa
Ichigaku Takigawa
Kyoto University / Hokkaido University
Verified email at kyoto-u.ac.jp - Homepage
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Cited by
Cited by
Year
Similarity-based machine learning methods for predicting drug–target interactions: a brief review
H Ding, I Takigawa, H Mamitsuka, S Zhu
Briefings in bioinformatics 15 (5), 734-747, 2014
4662014
Machine learning for catalysis informatics: recent applications and prospects
T Toyao, Z Maeno, S Takakusagi, T Kamachi, I Takigawa, K Shimizu
Acs Catalysis 10 (3), 2260-2297, 2019
4482019
Toward effective utilization of methane: machine learning prediction of adsorption energies on metal alloys
T Toyao, K Suzuki, S Kikuchi, S Takakusagi, K Shimizu, I Takigawa
The Journal of Physical Chemistry C 122 (15), 8315-8326, 2018
1812018
Machine-learning prediction of the d-band center for metals and bimetals
I Takigawa, K Shimizu, K Tsuda, S Takakusagi
RSC advances 6 (58), 52587-52595, 2016
1622016
Density functional theory calculations of oxygen vacancy formation and subsequent molecular adsorption on oxide surfaces
Y Hinuma, T Toyao, T Kamachi, Z Maeno, S Takakusagi, S Furukawa, ...
The Journal of Physical Chemistry C 122 (51), 29435-29444, 2018
1552018
A spectral clustering approach to optimally combining numericalvectors with a modular network
M Shiga, I Takigawa, H Mamitsuka
Proceedings of the 13th ACM SIGKDD international conference on Knowledge …, 2007
1322007
Obesity suppresses cell-competition-mediated apical elimination of RasV12-transformed cells from epithelial tissues
A Sasaki, T Nagatake, R Egami, G Gu, I Takigawa, W Ikeda, T Nakatani, ...
Cell reports 23 (4), 974-982, 2018
1222018
Machine learning reveals orbital interaction in materials
TL Pham, H Kino, K Terakura, T Miyake, K Tsuda, I Takigawa, HC Dam
Science and technology of advanced materials 18 (1), 756, 2017
1122017
Performance analysis of minimum/spl lscr//sub 1/-norm solutions for underdetermined source separation
I Takigawa, M Kudo, J Toyama
IEEE transactions on signal processing 52 (3), 582-591, 2004
1092004
Graph mining: procedure, application to drug discovery and recent advances
I Takigawa, H Mamitsuka
Drug discovery today 18 (1-2), 50-57, 2013
842013
Statistical analysis and discovery of heterogeneous catalysts based on machine learning from diverse published data
K Suzuki, T Toyao, Z Maeno, S Takakusagi, K Shimizu, I Takigawa
ChemCatChem 11 (18), 4537-4547, 2019
782019
CaMPDB: a resource for calpain and modulatory proteolysis
D duVERLE, I TAKIGAWA, Y ONO, H SORIMACHI, H MAMITSUKA
Genome Informatics 2009: Genome Informatics Series Vol. 22, 202-213, 2010
612010
Mining significant tree patterns in carbohydrate sugar chains
K Hashimoto, I Takigawa, M Shiga, M Kanehisa, H Mamitsuka
Bioinformatics 24 (16), i167-i173, 2008
592008
MED26 regulates the transcription of snRNA genes through the recruitment of little elongation complex
H Takahashi, I Takigawa, M Watanabe, D Anwar, M Shibata, ...
Nature communications 6 (1), 5941, 2015
552015
Predictions of cleavability of calpain proteolysis by quantitative structure-activity relationship analysis using newly determined cleavage sites and catalytic efficiencies of …
F Shinkai-Ouchi, S Koyama, Y Ono, S Hata, K Ojima, M Shindo, M Ueno, ...
Molecular & Cellular Proteomics 15 (4), 1262-1280, 2016
542016
Field independent probabilistic model for clustering multi-field documents
S Zhu, I Takigawa, J Zeng, H Mamitsuka
Information Processing & Management 45 (5), 555-570, 2009
532009
Machine learning reveals orbital interaction in materials
T Lam Pham, H Kino, K Terakura, T Miyake, K Tsuda, I Takigawa, ...
Science and technology of advanced materials 18 (1), 756-765, 2017
492017
Mining significant substructure pairs for interpreting polypharmacology in drug-target network
I Takigawa, K Tsuda, H Mamitsuka
PloS one 6 (2), e16999, 2011
472011
Dual graph convolutional neural network for predicting chemical networks
S Harada, H Akita, M Tsubaki, Y Baba, I Takigawa, Y Yamanishi, ...
BMC bioinformatics 21, 1-13, 2020
452020
Analysis of updated literature data up to 2019 on the oxidative coupling of methane using an extrapolative machine‐learning method to identify novel catalysts
S Mine, M Takao, T Yamaguchi, T Toyao, Z Maeno, SMA Hakim Siddiki, ...
ChemCatChem 13 (16), 3636-3655, 2021
442021
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