Tatsuya Akutsu
Tatsuya Akutsu
Professor, Bioinformatics Center, Institute for Chemical Research, Kyoto University
Verified email at
Cited by
Cited by
Identification of genetic networks from a small number of gene expression patterns under the Boolean network model
T Akutsu, S Miyano, S Kuhara
Biocomputing'99, 17-28, 1999
Control of Boolean networks: Hardness results and algorithms for tree structured networks
T Akutsu, M Hayashida, WK Ching, MK Ng
Journal of theoretical biology 244 (4), 670-679, 2007
Inferring qualitative relations in genetic networks and metabolic pathways
T Akutsu, S Miyano, S Kuhara
Bioinformatics 16 (8), 727-734, 2000
Protein homology detection using string alignment kernels
H Saigo, JP Vert, N Ueda, T Akutsu
Bioinformatics 20 (11), 1682-1689, 2004
Dynamic programming algorithms for RNA secondary structure prediction with pseudoknots
T Akutsu
Discrete Applied Mathematics 104 (1-3), 45-62, 2000
iLearn: an integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA, RNA and protein sequence data
Z Chen, P Zhao, F Li, TT Marquez-Lago, A Leier, J Revote, Y Zhu, ...
Briefings in bioinformatics 21 (3), 1047-1057, 2020
PROSPER: an integrated feature-based tool for predicting protease substrate cleavage sites
J Song, H Tan, AJ Perry, T Akutsu, GI Webb, JC Whisstock, RN Pike
PloS one 7 (11), e50300, 2012
IPknot: fast and accurate prediction of RNA secondary structures with pseudoknots using integer programming
K Sato, Y Kato, M Hamada, T Akutsu, K Asai
Bioinformatics 27 (13), i85-i93, 2011
Extensions of marginalized graph kernels
P Mahé, N Ueda, T Akutsu, JL Perret, JP Vert
Proceedings of the twenty-first international conference on Machine learning, 70, 2004
Algorithms for identifying Boolean networks and related biological networks based on matrix multiplication and fingerprint function
T Akutsu, S Miyano, S Kuhara
Proceedings of the fourth annual international conference on Computational …, 2000
Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics): Preface
M Abe, K Aoki, G Ateniese, R Avanzi, Z Beerliová, O Billet, A Biryukov, ...
Lecture Notes in Computer Science (including subseries Lecture Notes in …, 2006
Graph kernels for molecular structure− activity relationship analysis with support vector machines
P Mahé, N Ueda, T Akutsu, JL Perret, JP Vert
Journal of chemical information and modeling 45 (4), 939-951, 2005
iProt-Sub: a comprehensive package for accurately mapping and predicting protease-specific substrates and cleavage sites
J Song, Y Wang, F Li, T Akutsu, ND Rawlings, GI Webb, KC Chou
Briefings in bioinformatics 20 (2), 638-658, 2019
Identification of gene regulatory networks by strategic gene disruptions and gene overexpressions
T Akutsu, S Kuhara, O Maruyama, S Miyano
SODA 98, 695-702, 1998
A novel representation of protein sequences for prediction of subcellular location using support vector machines
S Matsuda, JP Vert, H Saigo, N Ueda, H Toh, T Akutsu
Protein Science 14 (11), 2804-2813, 2005
Dominating scale-free networks with variable scaling exponent: heterogeneous networks are not difficult to control
JC Nacher, T Akutsu
New Journal of Physics 14 (7), 073005, 2012
A system for identifying genetic networks from gene expression patterns produced by gene disruptions and overexpressions
T Akutsu, S Kuhara, O Maruyama, S Miyano
Genome Informatics 9, 151-160, 1998
Cascleave: towards more accurate prediction of caspase substrate cleavage sites
J Song, H Tan, H Shen, K Mahmood, SE Boyd, GI Webb, T Akutsu, ...
Bioinformatics 26 (6), 752-760, 2010
Quokka: a comprehensive tool for rapid and accurate prediction of kinase family-specific phosphorylation sites in the human proteome
F Li, C Li, TT Marquez-Lago, A Leier, T Akutsu, AW Purcell, A Ian Smith, ...
Bioinformatics 34 (24), 4223-4231, 2018
Algorithms for inferring qualitative models of biological networks
Biocomputing 2000, 293-304, 1999
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