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Topological Dynamics in Metamodel Discovery with Artificial Intelligence: From Biomedical to Cosmological Technologies [Hardback]

  • Formāts: Hardback, 210 pages, height x width: 234x156 mm, weight: 512 g, 13 Line drawings, color; 58 Line drawings, black and white; 3 Halftones, color; 15 Halftones, black and white; 16 Illustrations, color; 73 Illustrations, black and white
  • Sērija : Chapman & Hall/CRC Artificial Intelligence and Robotics Series
  • Izdošanas datums: 21-Dec-2022
  • Izdevniecība: Chapman & Hall/CRC
  • ISBN-10: 103236632X
  • ISBN-13: 9781032366326
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Topological Dynamics in Metamodel Discovery with Artificial Intelligence: From Biomedical to Cosmological TechnologiesPalielināt
  • Formāts: Hardback, 210 pages, height x width: 234x156 mm, weight: 512 g, 13 Line drawings, color; 58 Line drawings, black and white; 3 Halftones, color; 15 Halftones, black and white; 16 Illustrations, color; 73 Illustrations, black and white
  • Sērija : Chapman & Hall/CRC Artificial Intelligence and Robotics Series
  • Izdošanas datums: 21-Dec-2022
  • Izdevniecība: Chapman & Hall/CRC
  • ISBN-10: 103236632X
  • ISBN-13: 9781032366326
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9781032366326.html
Keywords:
The leveraging of artificial intelligence (AI) for model discovery in dynamical systems is cross-fertilizing and revolutionizing both disciplines, heralding a new era of data-driven science. This book is placed at the forefront of this endeavor, taking model discovery to the next level.

Dealing with artificial intelligence, this book delineates AIs role in model discovery for dynamical systems. With the implementation of topological methods to construct metamodels, it engages with levels of complexity and multiscale hierarchies hitherto considered off limits for data science.

Key Features:





Introduces new and advanced methods of model discovery for time series data using artificial intelligence Implements topological approaches to distill "machine-intuitive" models from complex dynamics data Introduces a new paradigm for a parsimonious model of a dynamical system without resorting to differential equations Heralds a new era in data-driven science and engineering based on the operational concept of "computational intuition"

Intended for graduate students, researchers, and practitioners interested in dynamical systems empowered by AI or machine learning and in their biological, engineering, and biomedical applications, this book will represent a significant educational resource for people engaged in AI-related cross-disciplinary projects.
Preface xiii
About the Author xvii
Part I Fundamentals
Chapter 1 Artificial Intelligence and Dynamical Systems
3(28)
1.1 Artificial Intelligence For Model Discovery
3(1)
1.2 Primer On Deep Learning
4(4)
1.3 Neural Networks As Models For Dynamical Systems
8(1)
1.4 Deep Learning With Biomedical Applications
9(1)
1.5 Convolutional Neural Networks In Drug Design
10(2)
1.6 Deep Learning For Dynamic Targets In Biomedicine
12(1)
1.7 Deep Learning Has Solved One Of The Protein Folding Problems
13(1)
1.8 Ai-Empowered Metamodel Discovery For Hierarchical Dynamical Systems: Adiabatic Regimes, Latent Manifolds, Quotient Spaces
14(7)
1.9 Metadynamics For Metamodels: Mapping Out The Quotient Manifold With A Dedicated Autoencoder
21(5)
1.10 Metamodels for the Digital Mind
26(5)
References
27(4)
Chapter 2 Topological Methods for Metamodel Discovery with Artificial Intelligence
31(32)
2.1 Ai-Based Metamodel Discovery For Hierarchically Complex Dynamical Systems
31(3)
2.2 Autoencoders Of Latent Coordinates In Dynamical Systems
34(3)
2.3 Deep Learning Scheme To Discover Underlying Differential Equations From Time Series
37(3)
2.4 Autoencoders For Molecular Dynamics Of Biological Matter
40(5)
2.5 Topological Dynamics On Latent Manifolds: Metamodels Without Equations
45(4)
2.6 Unraveling Topological Quotient Spaces For Dynamical Systems Metamodeling
49(3)
2.7 Learning To Encode And Propagate Topological Dynamics: Metamodel For Ubiquitin Folding In The Cell
52(3)
2.8 Metamodels For Hierarchical Dynamics Discovered Through Autoencoder Batteries
55(8)
References
59(4)
Part II Applications
Chapter 3 Artificial Intelligence Reverse-Engineers In Vivo Protein Folding
63(34)
3.1 Deconstructing In Vivo Protein Folding: Topological Metamodel Created By Transformer Technology
63(3)
3.2 Empowering Molecular Dynamics With Transformer Technology
66(6)
3.2.1 Propagating the Topological Dynamics in Textual Form with a Transformer Neural Network
66(4)
3.2.2 Topological Metamodeling Requires Two Autoencoders and a Transformer
70(2)
3.3 Protein Folding As A Textually Encodable Dynamical Metamodel: Mathematical Validation
72(3)
3.4 Injecting In Vivo Reality Into The Transformer-Generated Metamodel
75(1)
3.5 Propagating In Vitro Folding Pathways
76(2)
3.6 Atomistic Md Simulation Of An In Vivo Folding Setting
78(1)
3.7 Propagation Of In Vivo Folding Trajectories Using Transformer Technology
79(1)
3.8 The Al Platform To Generate In Vivo Folding Pathways
80(2)
3.9 Reverse-Engineering The Expeditious In Vivo Context I: Iterative Annealing In The Apo Groel Chamber
82(2)
3.10 Reverse-Engineering The In Vivo Context II: Groel Chamber In The (Atp)7 State
84(4)
3.11 In Vivo Pathways For Protein Folding: Ai Metamodels Live Up To The Challenge
88(2)
3.12 Metamodels With Implicit Content: Co-Translational Protein Folding
90(7)
References
94(3)
Chapter 4 The Drug-Induced Protein Folding Problem: Metamodels for Dynamic Targeting
97(22)
4.1 Protein Structure Is A Dynamic Object: Lesson For Targeted Therapy
97(2)
4.2 Deep Learning To Target Moving Targets In Molecular Therapy
99(3)
4.3 Ai-Based Metamodel To Infer Drug-Induced Folds In Targeted Proteins
102(6)
4.4 Learning To Induce Folds In Targeted Proteins
108(1)
4.5 Experimental Corroboration Of The Dynamic Metamodel For Drug-Induced Folding
108(3)
4.6 A Topological Metamodel Of The Induced Folding Dynamics Corroborates The Experimentally Validated Structural Adaptation In The Drug/Target Wbz_4/Jnk Complex
111(5)
4.7 Ai Teaches Drug Designers How To Target Proteins By Exploiting A Dynamic Metamodel Of Induced Folding
116(3)
References
116(3)
Chapter 5 Targeting Protein Structure in the Absence of Structure: Metamodels for Biomedical Applications
119(26)
5.1 Therapeutic Disruption Of Dysfunctional Protein Complexes In The Absence Of Reported Structure
119(4)
5.2 Al Guides The Therapeutic Disruption Of A Dysfunctional Complex With No Reported Structure
123(1)
5.3 Regulatory Sites In The Epistructure Of A Protein Target
124(2)
5.4 Ai-Based Metamodel To Infer Regulation-Modulated Epitopes In The Absence Of Target Structure
126(2)
5.5 Structural Metamodel For Therapeutic Disruption Of Dysfunctional Deregulated Protein Complexes In The Absence Of Structure
128(5)
5.6 Experimentally Validating The Dynamic Metamodel Of The Target Protein Structure By Developing A Molecular Targeted Therapy For Heart Failure
133(3)
5.7 The Dynamic Metamodel Of Protein Structure Enables Discovery Of Biological Cooperativity
136(9)
References
141(4)
Chapter 6 Autoencoder as Quantum Metamodel of Gravity: Toward an AI-Based Cosmological Technology
145(18)
6.1 The Quest For Quantum Gravity
145(5)
6.2 Quantum Gravity Autoencoder For A Neural Network With Emergent Gravity
150(4)
6.3 Relativistic Strings-Turned Quanta In Machine Learning Physics
154(1)
6.4 The Universe As A Variational Autoencoder
155(2)
6.5 Quantum Gravity Autoencoders And The Origin Of The Universe
157(3)
6.6 Technologies For Cosmological Manipulation Leveraging Quantum Gravity Autoencoders
160(3)
References
161(2)
Epilogue
163(12)
E.1 Topological Metamodels Breed Computational Intuition
163(2)
E.2 Ai Probes An Equivalence Between "Wormhole" And Quantum Entanglement
165(2)
E.3 Emergent Space-Time Topology Created By Entangling Quantum-Gravity Autoencoders
167(1)
E.4 Metamodel Discovery With Extra Dimensions And Lost Symmetries: Artificial Intelligence Deconstructs Quantum Mechanics
168(7)
References
174(1)
Appendix
175(30)
A.1 Code For Dehydron Identification
175(8)
A.2 Machine Learning Method To Infer Structure Wrapping And Dehydron Pattern In The Absence Of Protein Structure: The Twilighter
183(5)
A.3 Al Platform To Empower Molecular Dynamics
188(13)
A.3.1 Dynamical Feature Extraction for AI-Empowered Protein Folding Simulations
189(4)
A.3.2 How to Extend Molecular Dynamics Simulations
193(2)
A.3.3 AI-Generated Protein Folding Pathways
195(6)
A.3.4 Molecular Dynamics Run from an Al Platform
201(1)
A.4 Protein Folding Pathway Generated By Transformer-Engendered Topological Dynamics
201(1)
A.5 Endowing The Quantum Mechanics Autoencoder With Emergent Gravity
202(2)
A.6 Incorporating An Extra Dimension In Space-Time Through An Autoencoder Of The Standard Model: Decoding The Higgs Mechanism
204(1)
References 205(2)
Index 207
Ariel Fernįndez is an Argentine-American physical chemist and mathematician. He obtained a Ph. D. degree in Chemical Physics from Yale University and held the Hasselmann Endowed Chair Professorship in Bioengineering at Rice University until his retirement. To date, he has published over 400 scientific papers in professional journals including PNAS, Nature, Nature Biotechnology, Physical Review Letters, Genome Research and Genome Biology. Fernįndez has also authored five books on biophysics and molecular medicine and holds several patents on technological innovation. Since 2018 Fernįndez heads the Daruma Institute for Applied Intelligence, the research arm of AF Innovation, a Consultancy based in Argentina and the USA.