Precision medicine

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 & -- precision medicine -- 3. Deep phenotyping & -- precision medicine -- 4. Discussion & -- 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.

2.3. New avenues of personalized treatment in genetic epilepsy -- 2.3.1. Human induced pluripotent stem cell (iPSC) based-disease model -- 2.3.2. Oligonucleotides (ASOs) -- 2.3.3. Clustered regularly interspaced short palindromic repeats-associated protein 9 (CRISPR-Cas9)-derived model -- 3. Precision therapy with personalized pharmacogenomics -- 4. Precision therapy with neuromodulation -- 4.1. Responsive neurostimulation (RNS system) -- 4.2. Deep brain stimulation (DBS) -- 5. Conclusion -- Funding -- References -- Chapter Seven: EGFR, NF-κB and noncoding RNAs in precision medicine -- 1. Part 1: Epidermal growth factor receptor (EGFR) -- 2. EGFR as a target in lung cancer treatment -- 3. Acquired resistance of EGFR TKIs -- 3.1. First-generation EGFR TKIs -- 3.2. Second-generation EGFR TKIs -- 3.3. Third-generation EGFR TKIs -- 4. Precision medicine of EGFR in lung cancer -- 5. Part 2: Nuclear factor-κappa B signaling -- 6. NF-κB signaling pathway and its activation -- 6.1. Canonical NF-κB pathway -- 6.2. Non-canonical NF-κB pathway -- 6.3. EGFR and NF-κB -- 6.4. Noncoding RNAs -- 7. Part 3: Noncoding RNAs and EGFR in precision medicine -- 7.1. miRNAs in EGFR-targeted therapy -- 7.2. LncRNAs and EGFR-TKIs -- 7.3. tRNA-derived RNA fragments and EGFR -- 7.4. Noncoding RNAs and NF-κB signaling in cancer -- 7.5. p50 and p65 -- 7.6. TNF-α -- 7.7. IκB -- 7.8. MyD88 -- 7.9. CBM complex -- 7.10. TAK1 -- 7.11. Other ncRNAs and NF-κB signal -- 7.12. NF-κB signaling in chronic inflammation to cancer -- 8. Conclusion and future perspectives -- References -- Chapter Eight: Evolution of biomarker research in autoimmunity conditions for health professionals and clinical practice -- 1. Introductory -- 2. Abs with catalytic activity (catAbs or abzymes).

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.