Chemical timing 4. The rovibronic level structure associated with intramolecular vibrational redistribution in S1 p‐difluorobenzene

1986 ◽  
Vol 84 (3) ◽  
pp. 1119-1132 ◽  
Author(s):  
D. A. Dolson ◽  
K. W. Holtzclaw ◽  
D. B. Moss ◽  
C. S. Parmenter
Keyword(s):  
Level Structure ◽  
Rovibronic Level

Knowledge representation in graph-models of complex technical systems

2020 ◽  
pp. 12-16
Author(s):  
Evgenia R. Muntyan
Keyword(s):  
Analytical Study ◽  
Level Structure ◽  
Graph Models ◽  
Level Data ◽  
Knowledge Formation ◽  
Different Types ◽  
Level Information ◽  
Complex Technical Systems ◽  
Technical Systems ◽  
Structure Of Knowledge

The article analyzes a number of methods of knowledge formation using various graph models, including oriented, undirected graphs with the same type of edges and graphs with multiple and different types of edges. This article shows the possibilities of using graphs to represent a three-level structure of knowledge in the field of complex technical systems modeling. In such a model, at the first level, data is formed in the form of unrelated graph vertices, at the second level – information presented by a related undirected graph, and at the third level – knowledge in the form of a set of graph paths. The proposed interpretation of the structure of knowledge allows to create new opportunities for analytical study of knowledge and information, their properties and relationships.

scholarly journals Integrating Multi–Omics Data for Gene-Environment Interactions

BioTech ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 3
Author(s):  
Yinhao Du ◽  
Kun Fan ◽  
Xi Lu ◽  
Cen Wu
Keyword(s):  
Variable Selection ◽  
Complex Disease ◽  
Level Structure ◽  
Alternative Methods ◽  
Integrative Model ◽  
Omics Data ◽  
Promising Alternative ◽  
Disease Outcomes ◽  
Interaction Studies ◽  
Gene Environment

Gene-environment (G×E) interaction is critical for understanding the genetic basis of complex disease beyond genetic and environment main effects. In addition to existing tools for interaction studies, penalized variable selection emerges as a promising alternative for dissecting G×E interactions. Despite the success, variable selection is limited in terms of accounting for multidimensional measurements. Published variable selection methods cannot accommodate structured sparsity in the framework of integrating multiomics data for disease outcomes. In this paper, we have developed a novel variable selection method in order to integrate multi-omics measurements in G×E interaction studies. Extensive studies have already revealed that analyzing omics data across multi-platforms is not only sensible biologically, but also resulting in improved identification and prediction performance. Our integrative model can efficiently pinpoint important regulators of gene expressions through sparse dimensionality reduction, and link the disease outcomes to multiple effects in the integrative G×E studies through accommodating a sparse bi-level structure. The simulation studies show the integrative model leads to better identification of G×E interactions and regulators than alternative methods. In two G×E lung cancer studies with high dimensional multi-omics data, the integrative model leads to an improved prediction and findings with important biological implications.

scholarly journals Machine learning with persistent homology and chemical word embeddings improves prediction accuracy and interpretability in metal-organic frameworks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Aditi S. Krishnapriyan ◽  
Joseph Montoya ◽  
Maciej Haranczyk ◽  
Jens Hummelshøj ◽  
Dmitriy Morozov
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Metal Organic Framework ◽  
Persistent Homology ◽  
Level Structure ◽  
Chemical Information ◽  
Material System ◽  
Word Embeddings ◽  
Structure Property ◽  
Metal Organic

AbstractMachine learning has emerged as a powerful approach in materials discovery. Its major challenge is selecting features that create interpretable representations of materials, useful across multiple prediction tasks. We introduce an end-to-end machine learning model that automatically generates descriptors that capture a complex representation of a material’s structure and chemistry. This approach builds on computational topology techniques (namely, persistent homology) and word embeddings from natural language processing. It automatically encapsulates geometric and chemical information directly from the material system. We demonstrate our approach on multiple nanoporous metal–organic framework datasets by predicting methane and carbon dioxide adsorption across different conditions. Our results show considerable improvement in both accuracy and transferability across targets compared to models constructed from the commonly-used, manually-curated features, consistently achieving an average 25–30% decrease in root-mean-squared-deviation and an average increase of 40–50% in R2 scores. A key advantage of our approach is interpretability: Our model identifies the pores that correlate best to adsorption at different pressures, which contributes to understanding atomic-level structure–property relationships for materials design.

Energy level structure of Sc42

1965 ◽  
Vol 62 (3) ◽  
pp. 434-448 ◽  
Author(s):  
J.W. Nelson ◽  
J.D. Oberholtzer ◽  
H.S. Plendl
Keyword(s):  
Energy Level ◽  
Level Structure ◽  
Energy Level Structure
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