Cyclic Reduction of Symbolic Sequences

2019 ◽  
pp. 103-114
Author(s):  
Marlos A. G. Viana ◽  
Vasudevan Lakshminarayanan
Keyword(s):  
Cyclic Reduction ◽  
Symbolic Sequences

scholarly journals Memory order decomposition of symbolic sequences

2021 ◽  
Vol 104 (1) ◽  
Author(s):  
Unai Alvarez-Rodriguez ◽  
Vito Latora
Keyword(s):  
Symbolic Sequences

scholarly journals Nonlinear cyclic reduction for the analysis of mistuned cyclic systems

2021 ◽  
Vol 499 ◽  
pp. 116002
Author(s):  
Samuel Quaegebeur ◽  
Benjamin Chouvion ◽  
Fabrice Thouverez
Keyword(s):  
Cyclic Reduction ◽  
Cyclic Systems

scholarly journals Highly luminescent silver nanoclusters with tunable emissions: cyclic reduction–decomposition synthesis and antimicrobial properties

2013 ◽  
Vol 5 (2) ◽  
pp. e39-e39 ◽  
Author(s):  
Xun Yuan ◽  
Magdiel Inggrid Setyawati ◽  
Audrey Shu Tan ◽  
Choon Nam Ong ◽  
David Tai Leong ◽  
...  
Keyword(s):  
Antimicrobial Properties ◽  
Silver Nanoclusters ◽  
Cyclic Reduction

scholarly journals Physical complexity of symbolic sequences

2000 ◽  
Vol 137 (1-2) ◽  
pp. 62-69 ◽  
Author(s):  
C. Adami ◽  
N.J. Cerf
Keyword(s):  
Symbolic Sequences ◽  
Physical Complexity

Equivalence of the Markov chains and two-sided symbolic sequences

2006 ◽  
Vol 76 (6) ◽  
pp. 1015-1021 ◽  
Author(s):  
S. S Apostolov ◽  
Z. A Mayzelis ◽  
O. V Usatenko ◽  
V. A Yampol'skii
Keyword(s):  
Markov Chains ◽  
Symbolic Sequences

Kernel Generative Topographic Mapping of Protein Sequences

2012 ◽  
pp. 195-208
Author(s):  
M.I. Cardenas ◽  
A. Vellido ◽  
I. Olier ◽  
X. Rovira ◽  
J. Giraldo
Keyword(s):  
Data Analysis ◽  
Kernel Method ◽  
Protein Sequences ◽  
Topographic Mapping ◽  
Complex Data ◽  
Generative Topographic Mapping ◽  
Protein Amino Acid ◽  
Genomics And Proteomics ◽  
The World ◽  
Symbolic Sequences

The world of pharmacology is becoming increasingly dependent on the advances in the fields of genomics and proteomics. The –omics sciences bring about the challenge of how to deal with the large amounts of complex data they generate from an intelligence data analysis perspective. In this chapter, the authors focus on the analysis of a specific type of proteins, the G protein-couple receptors, which are the target for over 15% of current drugs. They describe a kernel method of the manifold learning family for the analysis of protein amino acid symbolic sequences. This method sheds light on the structure of protein subfamilies, while providing an intuitive visualization of such structure.

scholarly journals Preconditioning by incomplete block cyclic reduction

1984 ◽  
Vol 42 (166) ◽  
pp. 549-549 ◽  
Author(s):  
Garry Rodrigue ◽  
Donald Wolitzer
Keyword(s):  
Incomplete Block ◽  
Cyclic Reduction
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