Machine Learning for Structure-Rich Data-Scarce Domains

适用于结构丰富、数据稀缺领域的机器学习

• 批准号: 22K12150
• 负责人: NGUYEN Canh・Hao
• 金额: $ 2.66万
• 依托单位: Kyoto University
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (C)
• 财政年份: 2022
• 资助国家: 日本
• 起止时间: 2022-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-22K12150/
• 关键词: Graph neural networks; machine learning; Machine learning; Structured data; Deep learning; Sparse learning

We are working on the topic of predicting properties of two drugs, formulated as a pair of graphs. Due to its potential high dimensionality and small scale data, we have to leverage more data from different sources to avoid overfitting. The solution is to learn representation of two drugs within a network of drugs, proteins and other biological information.

我们正在研究的主题是预测两种药物的性质，用一对图表示。由于其潜在的高维度和小规模数据，我们必须利用来自不同来源的更多数据来避免过拟合。解决方案是在药物、蛋白质和其他生物信息的网络中学习两种药物的表示。