Retention Time Prediction for Chromatographic Enantioseparation by Quantile Geometry-enhanced Graph Neural Network.
Hao XuJinglong LinDongxiao ZhangFanyang MoPublished in: CoRR (2022)
Keyphrases
- neural network
- prediction model
- prediction accuracy
- prediction error
- graph structure
- graph theory
- long term
- directed graph
- multi layer perceptron
- artificial neural networks
- random walk
- neural network model
- graph representation
- prediction algorithm
- genetic algorithm
- graph based algorithm
- protein function prediction
- weighted graph
- three dimensional
- elman network
- directed acyclic graph
- neural network ensemble
- fuzzy logic
- pattern recognition
- knn
- d objects
- back propagation
- self organizing maps
- structured data
- network architecture
- feed forward
- associative memory
- graph theoretic
- edge weights
- graph matching
- recurrent neural networks
- probability distribution
- pairwise
- neural network is trained
- bipartite graph