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- W4290875398 abstract "Graphs (or networks) are ubiquitous representation in life sciences and medicine, from molecular interactions maps, signaling transduction pathways, to graphs of scientific knowledge and patient- disease-intervention relationships derived from population studies and/or real-world data, such as electronic health records and insurance claims. Recent advance in graph machine learning (ML) approaches such as graph neural networks (GNNs) has transformed a diverse set of problems relying on biomedical networks that traditionally depend on descriptive topological data analyses. Small- and macro- molecules that were not modeled as graphs also saw a bloom in GNN-based algorithms improving the state-of-the-art performance for learning their properties. Comparing to graph ML applications from other domains, life sciences offer many unique problems and nuances ranging from graph construction to graph- level, and bi-graph-level supervision tasks." @default.
- W4290875398 created "2022-08-12" @default.
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- W4290875398 date "2022-08-14" @default.
- W4290875398 modified "2023-10-16" @default.
- W4290875398 title "Graph Neural Networks in Life Sciences" @default.
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- W4290875398 doi "https://doi.org/10.1145/3534678.3542628" @default.
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