Cargando…
Highly accurate and large-scale collision cross sections prediction with graph neural networks
The collision cross section (CCS) values derived from ion mobility spectrometry can be used to improve the accuracy of compound identification. Here, we have developed the Structure included graph merging with adduct method for CCS prediction (SigmaCCS) based on graph neural networks using 3D confor...
Autores principales: | Guo, Renfeng, Zhang, Youjia, Liao, Yuxuan, Yang, Qiong, Xie, Ting, Fan, Xiaqiong, Lin, Zhonglong, Chen, Yi, Lu, Hongmei, Zhang, Zhimin |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10319785/ https://www.ncbi.nlm.nih.gov/pubmed/37402835 http://dx.doi.org/10.1038/s42004-023-00939-w |
Ejemplares similares
-
Ultra-fast and accurate electron ionization mass spectrum matching for compound identification with million-scale in-silico library
por: Yang, Qiong, et al.
Publicado: (2023) -
Collision-aware interactive simulation using graph neural networks
por: Zhu, Xin, et al.
Publicado: (2022) -
DeepGraphGO: graph neural network for large-scale, multispecies protein function prediction
por: You, Ronghui, et al.
Publicado: (2021) -
An Efficient Computational Model for Large-Scale Prediction of Protein–Protein Interactions Based on Accurate and Scalable Graph Embedding
por: Su, Xiao-Rui, et al.
Publicado: (2021) -
A survey of field programmable gate array (FPGA)-based graph convolutional neural network accelerators: challenges and opportunities
por: Li, Shun, et al.
Publicado: (2022)