Artificial intelligence-assisted diagnostic cytology and genomic testing for hematologic disorders
Artificial intelligence (AI) is a rapidly evolving field of computer science that involves the
development of computational programs that can mimic human intelligence. In particular …
development of computational programs that can mimic human intelligence. In particular …
Advanced analytical and informatic strategies for metabolite annotation in untargeted metabolomics
Liquid chromatography–mass spectrometry (LC–MS)-based untargeted metabolomics is
constantly challenged by large-scale and unambiguous metabolite annotation in complex …
constantly challenged by large-scale and unambiguous metabolite annotation in complex …
Machine learning-assisted identification of environmental pollutants by liquid chromatography coupled with high-resolution mass spectrometry
Chemical exposure can be linked with various adverse effects, but the causal association is
still poorly understood. To meet the challenge, non-target screening (NTS) based on liquid …
still poorly understood. To meet the challenge, non-target screening (NTS) based on liquid …
Neural network in food analytics
Neural network (ie deep learning, NN)-based data analysis techniques have been listed as
a pivotal opportunity to protect the integrity and safety of the global food supply chain and …
a pivotal opportunity to protect the integrity and safety of the global food supply chain and …
Constructing collective variables using invariant learned representations
On the time scales accessible to atomistic numerical modeling, chemical reactions are
considered rare events. Therefore, the atomistic simulations are commonly biased along a …
considered rare events. Therefore, the atomistic simulations are commonly biased along a …
Retention time prediction with message-passing neural networks
Retention time prediction, facilitated by advances in machine learning, has become a useful
tool in untargeted LC-MS applications. State-of-the-art approaches include graph neural …
tool in untargeted LC-MS applications. State-of-the-art approaches include graph neural …
Prediction of Retention Time and Collision Cross Section (CCSH+, CCSH–, and CCSNa+) of Emerging Contaminants Using Multiple Adaptive Regression Splines
Ultra-high performance liquid chromatography coupled to ion mobility separation and high-
resolution mass spectrometry instruments have proven very valuable for screening of …
resolution mass spectrometry instruments have proven very valuable for screening of …
Deep graph convolutional network for small-molecule retention time prediction
The retention time (RT) is a crucial source of data for liquid chromatography-mass
spectrometry (LCMS). A model that can accurately predict the RT for each molecule would …
spectrometry (LCMS). A model that can accurately predict the RT for each molecule would …
Retention Time Prediction through Learning from a Small Training Data Set with a Pretrained Graph Neural Network
Graph neural networks (GNNs) have shown remarkable performance in predicting the
retention time (RT) for small molecules. However, the training data set for a particular target …
retention time (RT) for small molecules. However, the training data set for a particular target …
Graph-Based Modeling and Molecular Dynamics for Ion Activity Coefficient Prediction in Polymeric Ion-Exchange Membranes
P Naghshnejad, G Theis Marchan… - Industrial & …, 2024 - ACS Publications
The partitioning of ions between polymeric ion-exchange membranes (IEMs) and the
surrounding liquid is governed by the activity coefficients of the ions, which, in turn …
surrounding liquid is governed by the activity coefficients of the ions, which, in turn …