Artificial Intelligence for Healthy Longevity

Preface
Contents
Contributors
Part I Biomarkers of Aging and Health
1 AI in Longevity
1.1 Statistical Models of Aging
1.1.1 Deep Learning Basic Principles
1.2 Deep Learning Aging Clocks
1.3 Deep Learning Applications in Medicine
1.3.1 Clinical Practice
1.3.2 Drug Development
References
2 Automated Reporting of Medical Diagnostic Imaging for Early Disease and Aging Biomarkers Detection
2.1 Longevity Medicine and Radiology
2.2 AI and Radiology: State of the Art
2.3 Proof of Concept
2.4 Future of AI in Longevity Medicine
References
3 Risk Forecasting Tools Based on the Collected Information for Two Types of Occupational Diseases
3.1 Development of a Mathematical Model Based on Risk Calculation Methodology Using Artificial Intelligence Tools
3.2 Overview of the Methods Used
3.3 Feature Description of Objects
3.4 Separation of Features by the Type of Their Values
3.5 Separation of Features by Meaning
3.6 The Type of the Problem to Be Solved and the Target Variables
3.7 Trained Models and Their Results
3.8 Linear Regression Model
3.9 Determining the Risk of Sensorineural Hearing Loss
3.10 Determination of the Risk of Vibration Disease (Local Vibration)
3.11 Determination of the Risk of Vibration Disease (General Vibration)
3.12 The Decision Tree Model
3.13 Determining the Risk of Sensorineural Hearing Loss
3.14 Determination of the Risk of Vibration Disease (Local Vibration)
3.15 Determination of the Risk of Vibration Disease (General Vibration)
3.16 Random Forest Model
3.17 Determining the Risk of Sensorineural Hearing Loss
3.18 Determination of the Risk of Vibration Disease (Local Vibration)
3.19 Determination of the Risk of Vibration Disease (General Vibration)
3.20 Predictive Risk Model
3.21 Conclusions and Model Selection
3.22 An Example of an Explicitly Interpreted DSS Fragment Based on an Automated Generated and Optimized Model
3.23 Justification of Risk Prediction Error Estimation
3.24 The First Approach to Estimating the Risk Prediction Error
3.25 The Second Approach to Estimating the Error of the Risk Forecast
3.26 Conclusions
References
4 Obtaining Longevity Footprints in DNA Methylation Data Using Different Machine Learning Approaches
4.1 Introduction
4.2 Biological Age Regression with Machine Learning Models
4.2.1 Baseline Models
4.2.2 One-Tissue Epigenetic Clocks
4.2.3 Pan-Tissue Epigenetic Clocks
4.3 Machine Learning for Age-Related Diseases Classification
4.3.1 Cancer Classification
4.3.2 Phenotype Classification
4.3.3 Case–Control Classification
4.4 Unsupervised Learning for Cancer Differentiating
4.5 Conclusions
References
5 The Role of Assistive Technology in Regulating the Behavioural and Psychological Symptoms of Dementia
5.1 Introduction
5.2 Methodology
5.3 Results
5.3.1 Assistive Technologies and Dementia
5.3.2 Assistive Technology to Aid Communication
5.3.3 Assistive Technology to Aid Motor Behaviour
5.3.4 Assistive Technology to Aid Inappropriate Behaviours
5.3.5 Assistive Technology—Smart Homes
5.3.6 Assistive Technology—Further Artificial Intelligence
5.4 Discussion
5.5 Conclusion
References
6 Epidemiology, Genetics and Epigenetics of Biological Aging: One or More Aging Systems?
6.1 Introduction
6.2 An Overview of Aging Clocks Based on Biometric and Molecular Data
6.2.1 Telomere Length
6.2.2 DNA Methylation Aging Clocks
6.2.3 Blood Biomarker-Based Aging Clocks
6.2.4 Neuroimaging Based Brain Aging Clocks
6.3 Overlap of Aging Clocks
6.3.1 Epidemiological Overlap
6.3.2 Biological Overlap
6.4 Conclusions
References
7 Temporal Relation Prediction from Electronic Health Records Using Graph Neural Networks and Transformers Embeddings
7.1 Introduction
7.2 Methods
7.2.1 Graph Construction
7.2.2 Masked Language Modeling
7.2.3 Model
7.3 Results
7.4 Discussion and Future Work
References
8 In Silico Screening of Life-Extending Drugs Using Machine Learning and Omics Data
8.1 Introduction
8.2 Methods
8.2.1 Data Collection
8.2.2 Lines of Invertebrate Models and Keeping Conditions
8.2.3 Statistics and Reproducibility
8.2.4 Model
8.3 Results
8.3.1 Model Validation on Synthetic Data
8.3.2 Lifespan Tests
8.4 Discussion
References
9 An Overview of Kernel Methods for Identifying Genetic Association with Health-Related Traits
9.1 Introduction
9.2 Introduction to the Kernels Methods for Genomic Data Analysis
9.2.1 The Kernel Functions
9.2.2 The Main Idea of the Kernel Methods
9.2.3 The Kernel Trick
9.2.4 Distance Induced by the Function Kernel
9.3 Kernel Machine Regression for Multi-marker genetic Association Testing
9.3.1 Kernel Linear and Logistic Regression Models for Genetic Association Test
9.3.2 Kernel Linear Regression Models
9.3.3 Kernel Logistic Regression Model
9.3.4 Rare-Variants Association Genetic Test
9.3.5 Connection with Other Multi-markers Association Tests
9.4 Selection of Variables for Gene-Set Analysis Using Kernels Methods
9.5 Kernel Methods for Association Genetics Test with Multiples Phenotypes
9.5.1 Genetic Association Test for Multiple Phenotypes Analysis Based on Kernel Methods
9.5.2 Multivariate Kernel Machine Regression (MKMR)
9.5.3 Multi-trait Sequence Kernel Association Test (MSKAT)
9.5.4 Gene Association with Multiple Traits (GAMuT)
9.6 Kernel Methods for Censored Survival Outcomes in Genetics Association Studies
9.7 Conclusions
References
10 Artificial Intelligence Approaches for Skin Anti-aging and Skin Resilience Research
10.1 Introduction
10.2 Genetics
10.2.1 Skin Aging Genes
10.2.2 Long-Lived Individuals and Twin Studies
10.2.3 Telomere Shortening
10.2.4 Variety of Aging Clocks
10.3 Molecular Aging
10.3.1 Omics Approaches in the Aging Research
10.4 Drug Discovery for Skin Resilience
10.5 Microbiome
10.6 Skin 3D Models
10.7 Biophysical Markers
10.8 Skin Imaging
10.8.1 Skin Image and Video Processing
10.8.2 Morphology Features and aging Patterns
10.8.3 AI Systems for Facial Imaging
10.8.4 Discoloration
10.8.5 Skin Texture
10.8.6 Nails and Hair
10.8.7 Estimation of Age
10.8.8 Simulation of Aging
10.9 Psychology
10.10 The Future of Skin Anti-aging Is Personalized
References
Part II Perspectives and Challenges in Machine Learning Research of Aging and Longevity
11 AI in Genomics and Epigenomics
11.1 AI to Diagnose Monogenic Diseases
11.1.1 AI Helps to Call Genomic Variants from Massive Parallel Sequencing Data
11.1.2 AI for Clinical Interpretation of Genomic Variants
11.1.3 Interpretation of Genomic Data and Clinical Description of Patient’s Phenotype
11.2 Interpretation of Epigenetic Changes in Aging
References
12 The Utility of Information Theory Based Methods in the Research of Aging and Longevity
12.1 Introduction
12.2 Definitions and Applications of Information-Theoretical Methods for the Research of Aging and Aging-Related Ill Health
12.3 The Application of Information-Theoretical Methods for the Evaluation of Biological and Biomedical Boundaries or Thresholds
12.4 Using Information-Theoretical Methods for Risk Group Attribution
12.5 Utilizing Information-Theoretical Methods for Genomic Sequence Analysis
12.6 Conclusion
References
13 AI for Longevity: Getting Past the Mechanical Turk Model Will Take Good Data
References
14 Leveraging Algorithmic and Human Networks to Cure Human Aging: Holistic Understanding of Longevity via Generative Cooperative Networks, Hybrid Bayesian/Neural/Logical AI and Tokenomics-Mediated Crowdsourcing
14.1 Introduction
14.2 Aging as a Complex Network Process
14.3 The Generative Cooperative Network (GCN)
14.4 Emergent Signs in GCN
14.5 How Emergent Signs Interact with Dependent Typing in the GCN
14.6 Data Absorption in GCN
14.7 BayesExpert in GCN
14.8 How BayesExpert can Combine Separate Studies into Coherent Wholes
14.9 Model Combination via Quadratic Programming
14.10 Hand-Crafted Bayes Net Model of Individual Aging
14.11 OpenCog’s BioAtomspace
14.12 Types of Atoms in the BioAtomspace
14.13 Tokenomic Incentivization
14.14 Strategies for Addressing Longevity via the Crowdsourced GCN Meta-Model
References
Author Index
Subject Index