Multi-Omics Technology in Human Health and Diseases : Genomics, Epigenomics, Transcriptomics, Proteomics, Metabolomics, Radiomics, Multi-Omics.

Format:

Book

Author/Creator:

Macha, Muzafar A.

Contributor:

Bhat, Ajaz A.

masoodi, Tariq A.

Language:

English

Physical Description:

1 online resource (288 pages)

Edition:

1st ed.

Place of Publication:

Chantilly : Elsevier Science & Technology, 2025.

Summary:

Multi-Omics Technology in Human Health and Diseases: Genomics, Epigenomics, Transcriptomics, Proteomics, Metabolomics, Radiomics, Multi-omic provides a way forward in understanding complete disease etiology and prognosis. This unique book gives a comprehensive understanding of multi-omics technology and its applications in understanding complex human health and disease and not only introduces the technology, but also its use for biomarker identification, drug discovery, and disease prognostication. A comprehensive understanding of human health and diseases, particularly cancer, requires knowledge of molecular biomarkers at a multi-omics level, including genome, epigenome, transcriptome, proteome, and metabolome, which is meagerly available or highly lacking among the students and scientific researchers. This book covers all the aspects of multi-omics technology and how this technology can be implemented into scientific research and discovery.Multi-Omics Technology in Human Health and Diseases: Genomics, Epigenomics, Transcriptomics, Proteomics, Metabolomics, Radiomics, Multi-omic is the first resource to present the latest information on currently utilized multi-omics technology for big data interpretation and their applications in understanding the complex human pathobiology. This is the perfect resource for researchers, academia, students, as well as the industrial audience.- Presents the most recent advancements in multiomics technology for elucidating complex human pathobiology.- Offers comprehensive insights into understanding disease etiology and prognosis.- Introduces the principles of multiomics technology and its applications in biomarker identification, drug discovery, and disease prognostication.- Explores the tools and methodologies for integrating and interpreting multiomics data sets.- Introduces multiomics databases and visualization web portals.- Provides up-to-date information on the role of multiomics technology in understanding intricate human pathobiology.

Contents:

Front Cover

Multi-Omics Technology in Human Health and Diseases

Multi-Omics Technology in Human Health and Diseases: Genomics, Epigenomics, Transcriptomics, Proteomics, Metabolomics, Radiomics, Multi-omics

Copyright

Dedication

Contents

Contributors

Foreword

Preface

Acknowledgments

1 - Introduction to multi-omics technology

1. Introduction

2. Components of multi-omics technology

2.1 Genomics

2.1.1 DNA sequencing technologies

2.1.2 Structural and functional genomics

2.2 Transcriptomics

2.2.1 RNA sequencing

2.2.2 Gene expression analysis

2.3 Proteomics

2.4 Metabolomics

2.5 Epigenomics

2.5.1 DNA methylation

2.5.2 Histone modifications

3. Integrative analysis techniques

4. Applications of multi-omics technology

5. Challenges and future directions

6. Conclusion

References

2 - Multi-omics data tools and integration approaches

1.1 Multi-omics fields

2. Genomics

3. Genomic data encoding

3.1 Genomic data analysis and interpretation tools

3.1.1 Sequence alignment tools

3.1.2 Variant calling tools

3.1.3 Annotation tools

3.1.3.1 Structural gene annotation tools

3.1.3.2 Functional gene annotation tools

3.1.4 Genomic visualization tools

4. Transcriptomics

4.1 Transcriptomic data analysis and interpretation tools

4.2 Sequence alignment tools

4.2.1 Transcript assembly and quantification tools

4.2.2 Differential expression analysis tools

4.2.3 Pathway and functional analysis tools

5. Proteomics

5.1 Proteomic data encoding

5.2 Proteomic data analysis and interpretation tools

5.2.1 Protein identification tools

5.2.2 Protein quantification tools

5.2.3 Proteomic visualization tools

6. Metabolomics

6.1 Metabolomic data analysis tools

7. Integrated multi-omics tools

8. Conclusion.

References

3 - Navigating the multifaceted landscape of omics

2. Divisions of omics

3. Genomics

4. Epigenomics

5. Cell Free Epigenome Atlas (CFEA) (http://www.bio-data.cn/CFEA/)

6. Trancriptomics

7. Proteomics

7.1 The human proteoform project

8. Metabolomics

9. Human Metabolome Database (HMDB)

10. Single-cell multi-omics

11. Microbiomics

12. The Human Microbiome Project

13. Radiomics

14. Imperative for integrating omics data

15. Databases with heterogeneous information

16. The Cancer Genome Atlas (TCGA)

17. Therapeutic Applicable Research to Generate Effective Treatments (TARGET)

18. Conclusion

4 - Multiomics in cancer research

1.1 Genomics

1.2 Epigenomics

1.3 Metabolomics

1.4 Pharmacogenomics

1.5 Transcriptomics

1.6 Bioinformatics tools for cancer data analysis

2. Conclusions and future perspectives

5 - Multi-omics for disease subtyping and classification

1. Multi-omics role in infectious diseases subtyping and classification

1.1 Genomics and pharmacogenomics in infectious diseases subtyping

1.2 Epigenomics and transcriptomics in infectious diseases subtyping

1.3 Proteomics and metabolomics in infectious diseases subtyping

2. Multi-omics role in cancer subtyping and classification

2.1 Breast cancer

2.1.1 Genomics in breast cancer disease subtyping

2.1.2 Proteomics in breast cancer disease subtyping

2.1.3 Transcriptomics in breast cancer disease subtyping

2.1.4 Epigenomics in breast cancer disease subtyping

2.1.4.1 DNA methylation

2.1.4.2 Histone modifications

2.1.4.3 Noncoding RNA

2.2 Colorectal cancer

2.2.1 Genomics in colorectal cancer disease subtyping

2.2.2 Transcriptomics in colorectal cancer disease subtyping.

2.2.3 Epigenomics in colorectal cancer disease subtyping

2.2.3.1 DNA methylation

2.2.4 Lung cancer

2.2.5 Genomics in lung cancer disease subtyping

2.2.6 Transcriptomics in lung cancer disease subtyping

2.2.7 Brain tumors

2.2.8 Genomics in brain tumors disease subtyping

2.2.9 Transcriptomics in brain tumors disease subtyping

2.2.9.1 Glioblastoma multiforme (GBM) subtypes

2.2.9.2 Medulloblastoma molecular subgroups

2.2.9.3 Diffuse Intrinsic Pontine Glioma (DIPG)

2.2.10 Proteomics in brain tumors disease subtyping

2.2.11 Epigenetics in brain tumors disease subtyping

3. Multi-omics role in cardiovascular diseases subtyping and classification

3.1 Genomics and pharmacogenomics applications in cardiovascular diseases

3.2 Epigenomics and transcriptomics in cardiovascular disease subtyping

3.3 Proteomics and metabolomics in cardiovascular disease subtyping

4. Multi-omics role in neurodegenerative diseases subtyping and classification

4.1 Transcriptomics

4.2 Proteomics

5. Challenges and future directions in multi-omics for disease subtyping

6 - Multi-omics and biomarker discoveries in Alzheimer's disease

2. Risk factors

3. Signs and symptoms

4. Genetics

5. History

6. Prevalence

7. Genes

8. Diagnosis

9. Biomarkers

10. Multi-omics data

11. Genomics

12. Epigenomics

13. Transcriptomics

14. Proteomics

15. Metabolomics

16. Microbiomics

17. Multiomics strategy

18. Multi-omics data analysis in AD

19. Conclusion

7 - Multi-omics and drug development

2. Integration of multi-omics data in drug development

2.1 Data integration methods

2.1.1 Conceptual integration

2.1.2 Model-based integration

2.1.3 Statistical integration.

2.1.4 Network and pathway integration

2.2 Multi-omics data tools for integration

2.3 Challenges in multi-omics data integration

3. Application of multi-omics in drug target identification

3.1 Identifying novel drug targets

3.2 Biomarker discovery

3.3 Understanding disease mechanisms

4. Multi-omics in drug development phases

4.1 Preclinical drug development

4.2 Clinical trials

4.3 Postmarket surveillance

5. Personalized medicine and multiomics

5.1 Precision medicine and drug response

5.2 Multi-omics for patient stratification

5.3 Predictive modeling of drug efficacy and safety

6. Multi-omics in drug repositioning

6.1 Identification of new indications for existing drugs

6.2 Omics-driven drug repurposing strategies

7. Future perspectives

8. Conclusion

Funding

8 - Charting the cancer landscape with multiomic technologies

1.1 Cancer development and mechanisms

1.2 Impact of cancer

1.3 Cancer management strategies

1.3.1 Prevention

1.3.2 Early detection

1.3.3 Treatment

1.3.4 Palliative care

1.3.5 Ongoing research and future perspectives

2. The integration of omics science with cancer research

2.5 Multiomics

2.5.1 Multiomics technology

2.6 Methodology

2.6.1 Data generation

2.6.2 Data integration

2.6.3 Computational analysis

2.6.4 Validation

2.7 Computational frameworks for multiomics studies

2.7.1 iCluster

2.7.2 iOmicsPASS

2.7.3 SALMON (Survival Analysis Learning with Multi-Omics Neural networks)

2.7.4 SNF (Similarity Network Fusion)

2.7.5 NEMO (NEighborhood based Multi-Omics clustering)

2.7.6 MONET (Multi Omic clustering by Non-Exhaustive Types).

2.7.7 PARADIGM (PAthway Recognition Algorithm using Data Integration on Genomic Models)

2.7.8 LRAcluster (Low Rank Approximation based multi-omics data clustering)

2.7.9 BCC (Bayesian Consensus Clustering)

3. Multi-omics in different cancers

3.1 Lung cancer

3.2 Breast cancer

3.3 Gastric cancer

3.4 Glioblastoma

3.5 Acute myeloid leukemia

3.6 Pancreatic ductal adenocarcinoma

4. Applications

4.1 Biomarker discovery

4.2 Understanding tumor heterogeneity

4.3 Uncovering dysregulated pathways and networks

4.4 Predicting treatment response

4.5 Mechanistic insights and drug discovery

4.6 Advances in drug-target discovery

5. Advantages of omics-driven studies in cancer

5.1 Comprehensive view

5.2 Identification of biomarkers

5.3 Target discovery

5.4 Personalized medicine

5.5 Systems biology insights

5.6 Data integration and mining

9 - Multi-omics data for machine learning algorithms

2. Digital encoding of omics data

2.1.1 Example of genomic data

2.2.1 Example of transcriptomic data

2.3.1 Example of proteomic data

2.4 Epigenomics

2.4.1 Example of epigenomic data

2.5 Metabolomics

2.5.1 Example of metabolomic data

2.6 Microbiomics

2.6.1 Example of microbiome data

3. Acquisition of mult-omics data

4. Machine learning as a tool for data analysis and prediction

5. Challenges and strategies for using multi-omics data in machine learning models

5.1 Challenges for applying machine learning on multi-omics data

5.2 Strategies for handling multi-omics data challenges

5.2.1 Feature selection

5.2.2 Feature extraction

5.2.3 Data augmentation

5.2.4 Multi-omics data integration

6. Applications of machine learning in multi-omics data analysis.

7. Conclusion.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

9780443135965

0443135967

9780443135958

0443135959

OCLC:

1511107589