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...

Descripción completa

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
_version_ 1784854223412789248
author Rao, Jiahua
Zheng, Shuangjia
Yang, Yuedong
author_facet Rao, Jiahua
Zheng, Shuangjia
Yang, Yuedong
author_sort Rao, Jiahua
collection PubMed
description 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.
format Online
Article
Text
id pubmed-9768675
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Elsevier
record_format MEDLINE/PubMed
spelling pubmed-97686752022-12-22 Integrating supercomputing and artificial intelligence for life science Rao, Jiahua Zheng, Shuangjia Yang, Yuedong Patterns (N Y) People of Data 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. Elsevier 2022-12-09 /pmc/articles/PMC9768675/ /pubmed/36569549 http://dx.doi.org/10.1016/j.patter.2022.100653 Text en © 2022 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle People of Data
Rao, Jiahua
Zheng, Shuangjia
Yang, Yuedong
Integrating supercomputing and artificial intelligence for life science
title Integrating supercomputing and artificial intelligence for life science
title_full Integrating supercomputing and artificial intelligence for life science
title_fullStr Integrating supercomputing and artificial intelligence for life science
title_full_unstemmed Integrating supercomputing and artificial intelligence for life science
title_short Integrating supercomputing and artificial intelligence for life science
title_sort integrating supercomputing and artificial intelligence for life science
topic People of Data
url 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
work_keys_str_mv AT raojiahua integratingsupercomputingandartificialintelligenceforlifescience
AT zhengshuangjia integratingsupercomputingandartificialintelligenceforlifescience
AT yangyuedong integratingsupercomputingandartificialintelligenceforlifescience

Ejemplares similares