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Found 7 papers, 1 papers with code
A New Deep State-Space Analysis Framework for Patient Latent State Estimation and Classification from EHR Time Series Data
This framework enables learning, visualizing, and clustering of temporal changes in patient latent states related to disease progression.
An end-to-end framework for gene expression classification by integrating a background knowledge graph: application to cancer prognosis prediction
We applied this framework to cancer prognosis prediction using gene expression data and a biological network.
GraphIX: Graph-based In silico XAI(explainable artificial intelligence) for drug repositioning from biopharmaceutical network
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.
Individual health-disease phase diagrams for disease prevention based on machine learning
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.
Health improvement framework for planning actionable treatment process using surrogate Bayesian model
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.
Dynamic change of gene-to-gene regulatory networks in response to SARS-CoV-2 infection
This investigation process is indispensable for an understanding of how SARS-CoV-2 behaves in human host cells.
Efficient Construction Method for Phase Diagrams Using Uncertainty Sampling
Furthermore, we show that using the US approach, undetected new phase can be rapidly found, and smaller number of initial sampling points are sufficient.
Materials Science Computational Physics
