Ichigaku Takigawa
Ichigaku Takigawa
Verified email at icredd.hokudai.ac.jp
TitleCited byYear
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, 2013
1912013
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
1062007
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
992004
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
492010
Mining significant tree patterns in carbohydrate sugar chains
K Hashimoto, I Takigawa, M Shiga, M Kanehisa, H Mamitsuka
Bioinformatics 24 (16), i167-i173, 2008
482008
Graph mining: procedure, application to drug discovery and recent advances
I Takigawa, H Mamitsuka
Drug discovery today 18 (1-2), 50-57, 2013
432013
Annotating gene function by combining expression data with a modular gene network
M Shiga, I Takigawa, H Mamitsuka
Bioinformatics 23 (13), i468-i478, 2007
422007
Mining significant substructure pairs for interpreting polypharmacology in drug-target network
I Takigawa, K Tsuda, H Mamitsuka
PloS one 6 (2), e16999, 2011
402011
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
372016
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
342009
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, 5941, 2015
312015
Mining metabolic pathways through gene expression
T Hancock, I Takigawa, H Mamitsuka
Bioinformatics 26 (17), 2128-2135, 2010
262010
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
242018
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
222017
Efficiently mining δ-tolerance closed frequent subgraphs
I Takigawa, H Mamitsuka
Machine Learning 82 (2), 95-121, 2011
202011
Efficiently finding genome-wide three-way gene interactions from transcript-and genotype-data
M Kayano, I Takigawa, M Shiga, K Tsuda, H Mamitsuka
Bioinformatics 25 (21), 2735-2743, 2009
202009
Convex sets as prototypes for classifying patterns
I Takigawa, M Kudo, A Nakamura
Engineering Applications of Artificial Intelligence 22 (1), 101-108, 2009
202009
Probabilistic path ranking based on adjacent pairwise coexpression for metabolic transcripts analysis
I Takigawa, H Mamitsuka
Bioinformatics 24 (2), 250-257, 2007
152007
ROS-DET: robust detector of switching mechanisms in gene expression
M Kayano, I Takigawa, M Shiga, K Tsuda, H Mamitsuka
Nucleic acids research 39 (11), e74-e74, 2011
142011
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
132018
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Articles 1–20