The energy well connection graph: a use of curvilinear coordinates to achieve dimension reduction for graph representation of molecular conformation energy data

1987 ◽  
Vol 71 (1) ◽  
pp. 75-88 ◽  
Author(s):  
T. M. Creese ◽  
Gary L. Grunewald ◽  
Mary W. Creese
Keyword(s):  
Dimension Reduction ◽  
Molecular Conformation ◽  
Graph Representation ◽  
Curvilinear Coordinates ◽  
Energy Data ◽  
Connection Graph ◽  
Conformation Energy

scholarly journals CLUSTER ANALYSIS OF MEDICAL TEXT DOCUMENTS BY USING SEMI-CLUSTERING APPROACH BASED ON GRAPH REPRESENTATION

2018 ◽  
Vol 7 (3) ◽  
pp. 213-224
Author(s):  
Rafał Woźniak ◽  
Piotr Ożdżyński ◽  
Danuta Zakrzewska
Keyword(s):  
Cluster Analysis ◽  
Dimension Reduction ◽  
Classification Accuracy ◽  
Space Dimension ◽  
Graph Representation ◽  
Clustering Method ◽  
Medical Field ◽  
Text Documents ◽  
Multilabel Classification ◽  
Clustering Approach

The development of Internet resulted in an increasing number of online text re-positories. In many cases, documents are assigned to more than one class and automatic multi-label classification needs to be used. When the number of labels exceeds the number of the documents, effective label space dimension reduction may signifi-cantly improve classification accuracy, what is a major priority in the medical field. In the paper, we propose document clustering for label selection. We use semi-clustering method, by considering graph representation, where documents are represented by vertices and edge weights are calculated according to their mutual similarity. Assigning documents to semi-clusters helps in reducing number of labels, further used in multilabel classification process. The performance of the method is examined by experiments conducted on real medical datasets.

Graph Neural Networks for Prediction of Fuel Ignition Quality

2020 ◽  
Author(s):  
Artur Schweidtmann ◽  
Jan Rittig ◽  
Andrea König ◽  
Martin Grohe ◽  
Alexander Mitsos ◽  
...  
Keyword(s):  
Neural Networks ◽  
Octane Number ◽  
Molecular Graph ◽  
Chemical Properties ◽  
Graph Representation ◽  
Structure Property ◽  
Oxygenated Hydrocarbons ◽  
Physico Chemical ◽  
Ignition Quality ◽  
Graph Neural Networks

<div>Prediction of combustion-related properties of (oxygenated) hydrocarbons is an important and challenging task for which quantitative structure-property relationship (QSPR) models are frequently employed. Recently, a machine learning method, graph neural networks (GNNs), has shown promising results for the prediction of structure-property relationships. GNNs utilize a graph representation of molecules, where atoms correspond to nodes and bonds to edges containing information about the molecular structure. More specifically, GNNs learn physico-chemical properties as a function of the molecular graph in a supervised learning setup using a backpropagation algorithm. This end-to-end learning approach eliminates the need for selection of molecular descriptors or structural groups, as it learns optimal fingerprints through graph convolutions and maps the fingerprints to the physico-chemical properties by deep learning. We develop GNN models for predicting three fuel ignition quality indicators, i.e., the derived cetane number (DCN), the research octane number (RON), and the motor octane number (MON), of oxygenated and non-oxygenated hydrocarbons. In light of limited experimental data in the order of hundreds, we propose a combination of multi-task learning, transfer learning, and ensemble learning. The results show competitive performance of the proposed GNN approach compared to state-of-the-art QSPR models making it a promising field for future research. The prediction tool is available via a web front-end at www.avt.rwth-aachen.de/gnn.</div>

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