scholarly journals Advancing Exposure Science through Chemical Data Curation and Integration in the Comparative Toxicogenomics Database

2016 ◽  
Vol 124 (10) ◽  
pp. 1592-1599 ◽  
Author(s):  
Cynthia J. Grondin ◽  
Allan Peter Davis ◽  
Thomas C. Wiegers ◽  
Benjamin L. King ◽  
Jolene A. Wiegers ◽  
...  
Keyword(s):  
Data Curation ◽  
Chemical Data ◽  
Comparative Toxicogenomics Database ◽  
Exposure Science

scholarly journals ChemEx: information extraction system for chemical data curation

2012 ◽  
Vol 13 (S17) ◽  
Author(s):  
Atima Tharatipyakul ◽  
Somrak Numnark ◽  
Duangdao Wichadakul ◽  
Supawadee Ingsriswang
Keyword(s):  
Information Extraction ◽  
Data Curation ◽  
Chemical Data ◽  
Extraction System ◽  
Information Extraction System

scholarly journals Accessing an Expanded Exposure Science Module at the Comparative Toxicogenomics Database

2018 ◽  
Vol 126 (1) ◽  
pp. 014501 ◽  
Author(s):  
Cynthia J. Grondin ◽  
Allan Peter Davis ◽  
Thomas C. Wiegers ◽  
Jolene A. Wiegers ◽  
Carolyn J. Mattingly
Keyword(s):  
Comparative Toxicogenomics Database ◽  
Exposure Science

Freeze-dried dispersions for automated SEM analysis of individual submicron airborne particulates

1992 ◽  
Vol 50 (1) ◽  
pp. 408-409
Author(s):  
Karen A. Katrinak ◽  
David W. Brekke ◽  
John P. Hurley
Keyword(s):  
Air Pollutants ◽  
Size Range ◽  
Sem Analysis ◽  
Individual Particle ◽  
Chemical Data ◽  
Particle Analysis ◽  
Bulk Analysis ◽  
Airborne Particulates ◽  
Freeze Dried ◽  
Individual Particle Analysis

Individual-particle analysis is well established as an alternative to bulk analysis of airborne particulates. It yields size and chemical data on a particle-by-particle basis, information that is critical in predicting the behavior of air pollutants. Individual-particle analysis is especially important for particles with diameter < 1 μm, because particles in this size range have a disproportionately large effect on atmospheric visibility and health.

Fast Neural Network Engine for Natural Science Language Processing: A Drug-Search Case.

2020 ◽  
Author(s):  
Vadim V. Korolev ◽  
Artem Mitrofanov ◽  
Kirill Karpov ◽  
Valery Tkachenko
Keyword(s):  
Neural Network ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Natural Science ◽  
Therapeutic Agent ◽  
Semantic Relations ◽  
Chemical Data ◽  
Processing Methods ◽  
Modern Natural

The main advantage of modern natural language processing methods is a possibility to turn an amorphous human-readable task into a strict mathematic form. That allows to extract chemical data and insights from articles and to find new semantic relations. We propose a universal engine for processing chemical and biological texts. We successfully tested it on various use-cases and applied to a case of searching a therapeutic agent for a COVID-19 disease by analyzing PubMed archive.

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