Coupling Mixed Mode Chromatography/ESI Negative MS Detection with Message-Passing Neural Network Modeling for Enhanced Metabolome Coverage and Structural Identification

A key unmet need in metabolomics continues to be the specific, selective, accurate detection of traditionally difficult to retain molecules including simple sugars, sugar phosphates, carboxylic acids, and related amino acids. Designed to retain the metabolites of central carbon metabolism, this Mixed Mode (MM) chromatography applies varied pH, salt concentration and organic content to a positively charged quaternary amine polyvinyl alcohol stationary phase. This MM method is capable of separating glucose from fructose, and four hexose monophosphates a single chromatographic run. Coupled to a QExactive Orbitrap Mass Spectrometer with negative ESI, linearity, LLOD, %CV, and mass accuracy were assessed using 33 metabolite standards. The standards were linear on average >3 orders of magnitude (R2 > 0.98 for 30/33) with LLOD < 1 pmole (26/33), median CV of 12% over two weeks, and median mass accuracy of 0.49 ppm. To assess the breadth of metabolome coverage and better define the structural elements dictating elution, we injected 607 unique metabolites and determined that 398 are well retained. We then split the dataset of 398 documented RTs into training and test sets and trained a message-passing neural network (MPNN) to predict RT from a featurized heavy atom connectivity graph. Unlike traditional QSAR methods that utilize hand-crafted descriptors or pre-defined structural keys, the MPNN aggregates atomic features across the molecular graph and learns to identify molecular subgraphs that are correlated with variations in RTs. For sugars, sugar phosphates, carboxylic acids, and isomers, the model achieves a predictive RT error of <2 min on 91%, 50%, 77%, and 72% of held-out compounds from these subsets, with overall root mean square errors of 0.11, 0.34, 0.18, and 0.53 min, respectively. The model was then applied to rank order metabolite IDs for molecular features altered by GLS2 knockout in mouse primary hepatocytes.

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Cluster Forecasting of Corruption Using Nonlinear Autoregressive Models with Exogenous Variables (NARX)—An Artificial Neural Network Analysis

Sustainability ◽

10.3390/su132011366 ◽

2021 ◽

Vol 13 (20) ◽

pp. 11366

Author(s):

SeyedAli Ghahari ◽

Cesar Queiroz ◽

Samuel Labi ◽

Sue McNeil

Keyword(s):

Neural Network ◽

Time Series ◽

Artificial Neural Network ◽

Global Perspective ◽

Fine Tuning ◽

Neural Network Modeling ◽

Monolithic Group ◽

Artificial Neural ◽

Mean Square Errors ◽

Nonlinear Autoregressive

Any effort to combat corruption can benefit from an examination of past and projected worldwide trends. In this paper, we forecast the level of corruption in countries by integrating artificial neural network modeling and time series analysis. The data were obtained from 113 countries from 2007 to 2017. The study is carried out at two levels: (a) the global level, where all countries are considered as a monolithic group; and (b) the cluster level, where countries are placed into groups based on their development-related attributes. For each cluster, we use the findings from our previous study on the cluster analysis of global corruption using machine learning methods that identified the four most influential corruption factors, and we use those as independent variables. Then, using the identified influential factors, we forecast the level of corruption in each cluster using nonlinear autoregressive recurrent neural network models with exogenous inputs (NARX), an artificial neural network technique. The NARX models were developed for each cluster, with an objective function in terms of the Corruption Perceptions Index (CPI). For each model, the optimal neural network is determined by fine-tuning the hyperparameters. The analysis was repeated for all countries as a single group. The accuracy of the models is assessed by comparing the mean square errors (MSEs) of the time series models. The results suggest that the NARX artificial neural network technique yields reliable future values of CPI globally or for each cluster of countries. This can assist policymakers and organizations in assessing the expected efficacies of their current or future corruption control policies from a global perspective as well as for groups of countries.

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Communicative Representation Learning on Attributed Molecular Graphs

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2020/392 ◽

2020 ◽

Cited By ~ 3

Author(s):

Ying Song ◽

Shuangjia Zheng ◽

Zhangming Niu ◽

Zhang-hua Fu ◽

Yutong Lu ◽

...

Keyword(s):

Neural Network ◽

Message Passing ◽

Molecular Graph ◽

Representation Learning ◽

Generation Process ◽

Molecular Graphs ◽

Graph Modeling ◽

Proposed Model ◽

The One ◽

Graph Neural Networks

Constructing proper representations of molecules lies at the core of numerous tasks such as molecular property prediction and drug design. Graph neural networks, especially message passing neural network (MPNN) and its variants, have recently made remarkable achievements in molecular graph modeling. Albeit powerful, the one-sided focuses on atom (node) or bond (edge) information of existing MPNN methods lead to the insufficient representations of the attributed molecular graphs. Herein, we propose a Communicative Message Passing Neural Network (CMPNN) to improve the molecular embedding by strengthening the message interactions between nodes and edges through a communicative kernel. In addition, the message generation process is enriched by introducing a new message booster module. Extensive experiments demonstrated that the proposed model obtained superior performances against state-of-the-art baselines on six chemical property datasets. Further visualization also showed better representation capacity of our model.

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Cluster Forecasting of Corruption Using Nonlinear Autoregressive with Exogenous Variables (NARX) – An Artificial Neural Network Analysis

10.20944/preprints202108.0247.v1 ◽

2021 ◽

Author(s):

SeyedAli Ghahari ◽

Cesar Quieroz ◽

Samuel Labi ◽

Sue McNeil

Keyword(s):

Neural Network ◽

Time Series ◽

Artificial Neural Network ◽

Influential Factors ◽

Global Perspective ◽

Neural Network Modeling ◽

Monolithic Group ◽

Artificial Neural ◽

Mean Square Errors ◽

Nonlinear Autoregressive

Any effort to combat corruption can benefit from an examination of past and projected worldwide trends. In this paper, we forecast the level of corruption in countries by integrating an artificial neural network modeling and time series analysis. The data were obtained from 113 countries from 2007 to 2017. The study is carried out at two levels: (a) global level where all countries are considered as a monolithic group; and (b) cluster level, where countries are placed into groups based on their development-related attributes. For each cluster, we use the findings from our previous study on the cluster analysis of global corruption using machine learning methods that identified the four most influential corruption factors, and we use those as independent variables. Then, using the identified influential factors, we forecast the level of corruption in each cluster using a nonlinear autoregressive recurrent neural network with exogenous inputs (NARX), an artificial neural network technique. The NARX models were developed for each cluster, with the objective function in terms of the Corruption Perceptions Index (CPI). For each model, the optimal neural network is determined by finetuning the hyperparameters. The analysis was repeated for all countries as a single group. The accuracy of the models is assessed by comparing the mean square errors (MSE) of the time series models. The results suggest that the NARX artificial neural network technique yields reliable future values of CPI globally or for each cluster of countries. This can assist policymakers and organizations in assessing the expected efficacies of their current or future corruption control policies from a global perspective as well as for groups of countries.

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