Precision medicine

Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Teplow, David B.
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Cambridge, MA : Academic Press, 2022.
Colección:Progress in molecular biology and translational science ; v. 190
Temas:
Precision medicine.
Medical technology.
Precision Medicine
Medical Laboratory Science
M�edecine de pr�ecision.
Technologie m�edicale.
Medical technology
Precision medicine
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Intro
  • Precision Medicine
  • Copyright
  • Contents
  • Contributors
  • Preface
  • Chapter One: Introduction of medical genomics and clinical informatics integration for p-Health care
  • 1. Introduction
  • 1.1. Clinical informatics
  • 1.2. Genomic and genomic medicine
  • 2. State-of-the-art advanced data integration
  • 2.1. Raw data level integration
  • 2.2. Feature level integration
  • 2.3. Decision level integration
  • 3. Case studies
  • 3.1. Predicting length of stay after infant cardiac surgery by integrating proteomic data with genomic variations using m ...
  • 4. Privacy and security
  • 5. Conclusion
  • References
  • Chapter Two: Precision diagnostics in cancer: Predict, prevent, and personalize
  • 1. Introduction
  • 1.1. Somatic oncology testing
  • 1.2. Approaches to precision oncology
  • 1.3. Whole genome sequencing (WGS)
  • 1.4. Whole exome sequencing (WES)
  • 1.5. Targeted panels
  • 2. NGS-based technologies
  • 2.1. Step 1: Tumor sampling and extraction
  • 2.2. Step 2: Sequencing library generation
  • 2.3. Step 3: Sequencing
  • 2.4. Step 4: Data analysis
  • 2.5. Step 5: Variant interpretation
  • 3. Therapy selection
  • 3.1. Targeted therapies
  • 3.2. Checkpoint inhibitors and immunotherapies
  • 4. Clinical trial design
  • 5. Conclusions
  • References
  • Chapter Three: Artificial intelligence and machine learning in precision medicine: A paradigm shift in big data analysis
  • 1. Introduction
  • 2. Artificial intelligence and big data: Changes in the landscape of precision healthcare and medicine
  • 3. The emergence of AI in precision medicine
  • 4. Synergies between artificial intelligence and precision medicine
  • 4.1. Therapy planning
  • 4.1.1. Genomic considerations
  • 4.1.2. Environmental considerations
  • 4.2. Risk prediction and diagnosis
  • 4.2.1. Genomic approaches
  • 4.2.2. Nongenomic approaches.
  • 5. Promises of implementing AI/ML in healthcare
  • 5.1. Solving the human resource crisis in healthcare
  • 5.2. Improving workflow and reducing medical errors
  • 5.3. Enhancing healthcare delivery
  • 6. The integration of nanomaterials in AI-based precision medicine
  • 7. The perspective of big data analytics in precision medicine
  • 8. AI and personalized medicine: A case of neurological disease
  • 9. Obstacles in translating big data into healthcare
  • 10. Opportunities for translating big data into healthcare
  • 11. Artificial intelligence and machine learning in precision medicine: An industry perspective
  • 11.1. Multi-omics profiling
  • 11.2. Digital biomarkers and biomarker-based clinical trials
  • 11.3. Precision dosing and safety measures
  • 12. Future challenges and perspectives of artificial intelligence in precision medicine
  • Conflict of interest
  • Acknowledgment
  • Author's contribution
  • References
  • Chapter Four: Precision medicine with multi-omics strategies, deep phenotyping, and predictive analysis
  • 1. Introduction
  • 2. Multi-omics strategies &amp
  • precision medicine
  • 3. Deep phenotyping &amp
  • precision medicine
  • 4. Discussion &amp
  • future recommendations
  • Acknowledgments
  • Competing interests
  • Author contribution
  • Biographical note
  • Funding information
  • References
  • Chapter Five: Progress in molecular biology and translational science addressing the needs of nano-rare patients
  • 1. Introduction
  • 2. Parsing patient populations
  • 3. Mutation focused drug discovery
  • 4. Prerequisites to respond to the needs of nano-rare patients
  • 4.1. Genotype, phenotype, clinical investigator
  • 4.2. A drug discovery platform capable of rapidly creating personalized experimental treatments
  • 4.3. Rapidity
  • 4.4. Cost-effectiveness.
  • 4.5. Meeting any of the criteria summarized above is essentially impossible for most drug discovery technologies
  • 4.5.1. Versatility
  • 4.5.2. Predictability
  • 4.5.3. Scalability
  • 4.5.4. A supportive regulatory environment
  • 5. Creating an industrialized approach to the non-profit treatment of nano-rare patients
  • 5.1. Access to appropriately characterized patients
  • 5.2. Establishing quality systems
  • 5.3. Access to treatment
  • 5.4. Treatment goals and methods of assessment
  • 5.5. Baseline (natural history) characterization and evaluation of ASO treatment effects
  • 5.6. Analysis of aggregate performance and sharing experience with all stakeholders
  • 5.7. The optimal ASO for each patient
  • 5.8. Demand for treatment
  • 5.9. Creating a broad network of collaborators
  • 6. The future
  • 6.1. Broadening treatment opportunities
  • 6.2. Facilitating earlier diagnosis
  • 6.3. Reducing isolation
  • 7. Remaining challenges
  • References
  • Chapter Six: Precision medicine in epilepsy
  • 1. Introduction
  • 2. Precision therapy in genetic epilepsy syndromes
  • 2.1. Current genetic testing tools and their yield in epilepsy
  • 2.1.1. Microarray
  • 2.1.2. Targeted gene panel testing
  • 2.1.3. Whole-exome sequencing (WES) and whole-genome sequencing (WGS)
  • 2.1.4. Multiomic data in research
  • 2.2. Precision medicine approach in genetic epilepsy syndromes
  • 2.2.1. ALDH7A1/PNPO/PROSC
  • 2.2.2. ATP7A
  • 2.2.3. FOLR1
  • 2.2.4. GAMT/AGAT/SLC6A8 (creatinine deficiency syndromes)
  • 2.2.5. GRIN2A,B,D
  • 2.2.6. KCNT1
  • 2.2.7. KCNA2
  • 2.2.8. KCNQ2
  • 2.2.9. MOSC1/MOSC2/GPHN
  • 2.2.10. POLG1
  • 2.2.11. PRRT2
  • 2.2.12. SCN1A
  • 2.2.13. SCN2A
  • 2.2.14. SCN8A
  • 2.2.15. SLC2A1
  • 2.2.16. SLC6A1
  • 2.2.17. SLC19A3
  • 2.2.18. SLC35A2
  • 2.2.19. STXBP1
  • 2.2.20. TPP1
  • 2.2.21. TRPM6
  • 2.2.22. TSC1 and TSC2.
  • 3. Progressing toward personalized and precision healthcare services and challenges in applying biomarkers for clinical a ...
  • 3.1. Classical technological approaches to autoAb-based biomarker discovery and validation
  • 3.1.1. Diagnosis of asymptomatic disease
  • 3.1.2. Assessment and prediction of disease activity
  • 3.1.3. Dynamic biomarkers
  • 3.1.4. Assessment of drug toxicity
  • 3.1.5. Predicting responsiveness to therapy
  • 4. Specific autoimmunity conditions subareas
  • 4.1. Biomarkers in clinical practice: Biomarkers as applicable to autoimmune myocarditis
  • 4.2. Biomarkers in clinical practice: Biomarkers as applicable to multiple sclerosis
  • 4.3. Biomarkers in clinical practice: Biomarkers as applicable to type 1 diabetes
  • 4.3.1. Genetics of T1D and genomic biomarkers
  • 4.3.2. Non-HLA loci
  • 4.3.3. Biomarkers connected with hyperglycemia
  • 4.3.4. C-peptide
  • 4.3.5. AutoAbs as biomarkers
  • 4.3.6. Potential new types of biomarkers
  • 4.3.7. Proteomics analysis of serum is considered to be challenging
  • 4.3.8. The ratio of proinsulin to C-peptide
  • 4.3.9. Metabolomics biomarkers
  • 4.3.10. The new autoAgs and autoAbs
  • 4.3.11. Posttranslational modifications
  • 4.3.12. Cytokines as T1D biomarkers
  • 4.3.13. Nucleic acid-based predictive biomarkers
  • 4.3.14. Biomarkers of T1D complications
  • 4.4. Biomarkers in clinical practice: Biomarkers as applicable to SLE
  • 4.4.1. Epigenomic biomarkers
  • 4.4.2. Genomic biomarkers
  • 4.4.3. Proteomic biomarkers
  • 4.5. Biomarkers in clinical practice: Biomarkers as applicable to RA
  • 4.5.1. DNA methylation
  • 4.6. Biomarkers in clinical practice: Biomarkers as applicable to systemic sclerosis
  • 4.6.1. Proteomic biomarkers
  • 5. Biomarkers in clinical practice: Biomarkers as applicable to personalized dietary protocols in autoimmune conditions
  • 6. Conclusion
  • References
  • Index.

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