Integrating supercomputing and artificial intelligence for life science

Jiahua Rao and Shuangjia Zheng are Ph.D. students in Prof. Yang’s lab (Supercomputing And AI for Life science, SAIL Lab) at Sun Yat-sen University. They recently developed an interpretable framework to quantitatively assess the interpretability of Graph Neural Network (GNN) and made comparison with...

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Detalles Bibliográficos
Autores principales: Rao, Jiahua, Zheng, Shuangjia, Yang, Yuedong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
People of Data
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9768675/
https://www.ncbi.nlm.nih.gov/pubmed/36569549
http://dx.doi.org/10.1016/j.patter.2022.100653
Descripción
Sumario:Jiahua Rao and Shuangjia Zheng are Ph.D. students in Prof. Yang’s lab (Supercomputing And AI for Life science, SAIL Lab) at Sun Yat-sen University. They recently developed an interpretable framework to quantitatively assess the interpretability of Graph Neural Network (GNN) and made comparison with medicinal chemists. Their meaningful benchmarking and rigorous framework would greatly benefit development of new interpretable methods in GNNs.

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