1 code implementation • 12 Nov 2023 • Tatsuya Sagawa, Ryosuke Kojima
Transformer-based deep neural networks have revolutionized the field of molecular-related prediction tasks by treating molecules as symbolic sequences.
no code implementations • 21 Jul 2023 • Aya Nakamura, Ryosuke Kojima, Yuji Okamoto, Eiichiro Uchino, Yohei Mineharu, Yohei Harada, Mayumi Kamada, Manabu Muto, Motoko Yanagita, Yasushi Okuno
This framework enables learning, visualizing, and clustering of temporal changes in patient latent states related to disease progression.
no code implementations • 29 Jun 2023 • Kazuma Inoue, Ryosuke Kojima, Mayumi Kamada, Yasushi Okuno
We applied this framework to cancer prognosis prediction using gene expression data and a biological network.
no code implementations • 21 Dec 2022 • Atsuko Takagi, Mayumi Kamada, Eri Hamatani, Ryosuke Kojima, Yasushi Okuno
GraphIX is a framework for evidence-based drug discovery that can present to users new disease-drug associations and identify the protein important for understanding its pharmacological effects from a large and complex knowledge base.
1 code implementation • 27 Jun 2022 • Yuji Okamoto, Ryosuke Kojima
We propose a method to learn nonlinear systems guaranteeing the input-output stability.
no code implementations • 31 May 2022 • Kazuki Nakamura, Eiichiro Uchino, Noriaki Sato, Ayano Araki, Kei Terayama, Ryosuke Kojima, Koichi Murashita, Ken Itoh, Tatsuya Mikami, Yoshinori Tamada, Yasushi Okuno
Here, we present the health-disease phase diagram (HDPD), which represents a personal health state by visualizing the boundary values of multiple biomarkers that fluctuate early in the disease progression process.
no code implementations • 14 Aug 2021 • Taisuke Sato, Ryosuke Kojima
We propose a new approach to SAT solving which solves SAT problems in vector spaces as a cost minimization problem of a non-negative differentiable cost function J^sat.
no code implementations • 30 Oct 2020 • Kazuki Nakamura, Ryosuke Kojima, Eiichiro Uchino, Koichi Murashita, Ken Itoh, Shigeyuki Nakaji, Yasushi Okuno
A key point of the framework is the evaluation of the "actionability" for personal health improvements by using a surrogate Bayesian model in addition to a high-performance nonlinear ML model.
1 code implementation • 27 Mar 2019 • Yusuke Nagasaka, Akira Nukada, Ryosuke Kojima, Satoshi Matsuoka
We evaluated the performance of the GCNs application on TSUBAME3. 0 implementing NVIDIA Tesla P100 GPU, and our batched approach shows significant speedups of up to 1. 59x and 1. 37x in training and inference, respectively.
Distributed, Parallel, and Cluster Computing
no code implementations • 20 Jan 2019 • Ryosuke Kojima, Taisuke Sato
To embody this programming language, we also introduce a new semantics, termed tensorized semantics, which combines the traditional least model semantics in logic programming with the embeddings of tensors.
no code implementations • 19 Aug 2017 • Mizuho Nishio, Mitsuo Nishizawa, Osamu Sugiyama, Ryosuke Kojima, Masahiro Yakami, Tomohiro Kuroda, Kaori Togashi
TPE or random search was used for parameter optimization of SVM and XGBoost.