Tetsuya Sakurai

University of Tsukuba — Japan

Computer Science · Physical Sciences

26h-index3.2kcitations253works4.52yr avg
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Research Topics

Matrix Theory and Algorithms(83), Electromagnetic Scattering and Analysis(35), Face and Expression Recognition(27), Privacy-Preserving Technologies in Data(23), Advanced Optimization Algorithms Research(21)

Publications253 total

Anomaly detection in double-entry bookkeeping data by federated learning system with non-model sharing approach
Scientific Reports·2025Open Access
Modularity Regularized Locality Preserving Projection
Communications in computer and information science·2025
MOFormer: navigating the antimicrobial peptide design space with Pareto-based multi-objective transformer
Briefings in Bioinformatics·2025· 1 citedOpen Access
Hybrid quantum-classical-quantum convolutional neural networks
Scientific Reports·2025· 11 citedOpen Access
Estimation of conditional average treatment effects on distributed confidential data
Expert Systems with Applications·2025· 1 citedOpen Access
Deep Feature Learning for Multi-Omics Clustering Using Supervised Variational Autoencoders with Clinical Information
2025
A new type of federated clustering: A non-model-sharing approach
arXiv (Cornell University)·2025Open Access
Estimating Covariate-balanced Survival Curve in Distributed Data Environment using Data Collaboration Quasi-Experiment
arXiv (Cornell University)·2025Open Access
PKAN: Leveraging Kolmogorov–Arnold Networks and Multi-Modal Learning for Peptide Prediction With Advanced Language Models
IEEE Journal of Biomedical and Health Informatics·2025· 5 cited
Multi-Omics clustering by integrating clinical features from large language model
Methods·2025· 4 cited
Data collaboration for causal inference from limited medical testing and medication data
Scientific Reports·2025· 4 citedOpen Access
Development of a deep learning-based model to evaluate changes during radiotherapy using cervical cancer digital pathology
Journal of Radiation Research·2025· 3 citedOpen Access
MultiCycPermea: accurate and interpretable prediction of cyclic peptide permeability using a multimodal image-sequence model
BMC Biology·2025· 9 citedOpen Access
Anomaly Detection in Double-entry Bookkeeping Data by Federated Learning System with Non-model Sharing Approach
arXiv (Cornell University)·2025· 1 citedOpen Access
A New Type of Federated Clustering: A Non-Model-Sharing Approach
SSRN Electronic Journal·2025· 1 citedOpen Access
Selecting interpretable features for cancer subtyping on multi-omics data
2024· 1 cited
Integrating Biological Language Processing and Memory Attention Model for Protein-DNA Binding Residue Prediction
2024
Robust feature learning using contractive autoencoders for multi-omics clustering in cancer subtyping
Methods·2024· 6 cited
MOFormer: Multi-Objective Antimicrobial Peptide Generation through Conditional Transformer with Multi-Fusion Descriptors
2024· 2 citedOpen Access
FedDCL: a federated data collaboration learning as a hybrid-type privacy-preserving framework based on federated learning and data collaboration
arXiv (Cornell University)·2024Open Access
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Frequent Co-authors

Xiucai Ye(15), Akira Imakura(13), Hiroto Tadano(8), Yasunori Futamura(7), Tsutomu Ikegami(6), Hiroshi Sugiura(4), Lesong Wei(3), Leyi Wei(3), Quan Zou(3), Peter Kravanja(3), Marc Van Barel(3), Hongmin Li(3), Lei Du(3), Junko Asakura(2), Kinji Kimura(2), Umpei Nagashima(2), Xiaoqing Ru(2), T. Mizusaki(2), Yukihiko Okada(2), Weihang Zhang(2)