Tatsuya Akutsu

Employment

• Kyoto University Professor
  2001 - Present

Education

• University of Tokyo Dr. Engineering
  1986 - 1989

Projects & Funding

Projects & funding information is unavailable.

Publications (366)

• Extracting boolean and probabilistic rules from trained neural networks
  Neural Networks 2020 DOI: 10.1016/j.neunet.2020.03.024
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• A Novel Method for Inference of Chemical Compounds of Cycle Index Two with Desired Properties Based on Artificial Neural Networks and Integer Programming
  Algorithms 2020 DOI: 10.3390/a13050124
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• Procleave: Predicting Protease-Specific Substrate Cleavage Sites by Combining Sequence and Structural Information
  Genomics, Proteomics & Bioinformatics 2020 DOI: 10.1016/j.gpb.2019.08.002
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• Improved Hardness of Maximum Common Subgraph Problems on Labeled Graphs of Bounded Treewidth and Bounded Degree
  International Journal of Foundations of Computer Science 2020 DOI: 10.1142/S0129054120500069
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• An Overview of Bioinformatics Methods for Analyzing Autism Spectrum Disorders
  Current Pharmaceutical Design 2020 DOI: 10.2174/1381612825666191111154837
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• A Method for the Inverse QSAR/QSPR Based on Artificial Neural Networks and Mixed Integer Linear Programming
  ACM International Conference Proceeding Series 2020 DOI: 10.1145/3386052.3386054
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• A new integer linear programming formulation to the inverse qsar/qspr for acyclic chemical compounds using skeleton trees
  Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2020 DOI: 10.1007/978-3-030-55789-8_38
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• A novel method for the inverse QSAR/QSPR based on artificial neural networks and mixed integer linear programming with guaranteed admissibility
  BIOINFORMATICS 2020 - 11th International Conference on Bioinformatics Models, Methods and Algorithms, Proceedings; Part of 13th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2020 2020
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• Breast Cancer Subtype by Imbalanced Omics Data through A Deep Learning Fusion Model
  ACM International Conference Proceeding Series 2020 DOI: 10.1145/3386052.3386063
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• Causalcall: Nanopore Basecalling Using a Temporal Convolutional Network
  Frontiers in Genetics 2020 DOI: 10.3389/fgene.2019.01332
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