Search Results for author: Ryosuke Kojima

Found 11 papers, 3 papers with code

ReactionT5: a large-scale pre-trained model towards application of limited reaction data

1 code implementation12 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.

GraphIX: Graph-based In silico XAI(explainable artificial intelligence) for drug repositioning from biopharmaceutical network

no code implementations21 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.

Drug Discovery Explainable artificial intelligence +1

Learning Deep Input-Output Stable Dynamics

1 code implementation27 Jun 2022 Yuji Okamoto, Ryosuke Kojima

We propose a method to learn nonlinear systems guaranteeing the input-output stability.

Time Series Time Series Analysis

Individual health-disease phase diagrams for disease prevention based on machine learning

no code implementations31 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.

BIG-bench Machine Learning Disease Prediction

MatSat: a matrix-based differentiable SAT solver

no code implementations14 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.

Health improvement framework for planning actionable treatment process using surrogate Bayesian model

no code implementations30 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.

Clinical Knowledge Decision Making +1

Batched Sparse Matrix Multiplication for Accelerating Graph Convolutional Networks

1 code implementation27 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

A tensorized logic programming language for large-scale data

no code implementations20 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.

Knowledge Graphs

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