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Integrating Omics Data 1st Edition by George C Tseng, Debashis Ghosh, Xianghong Jasmine Zhou ISBN 1107069114 9781107069114

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Author(s): George C. Tseng, Debashis Ghosh, Xianghong Jasmine Zhou
ISBN(s): 9781107069114, 1107069114
Edition: 1
File Details: PDF, 18.09 MB
Year: 2015
Language: english
SKU: EB-5299890 Category: Tags: , ,

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ISBN 10: 1107069114 
ISBN 13: 9781107069114
Author: George C Tseng, Debashis Ghosh, Xianghong Jasmine Zhou

In most modern biomedical research projects, application of high-throughput genomic, proteomic, and transcriptomic experiments has gradually become an inevitable component. Popular technologies include microarray, next generation sequencing, mass spectrometry and proteomics assays. As the technologies have become mature and the price affordable, omics data are rapidly generated, and the problem of information integration and modeling of multi-lab and/or multi-omics data is becoming a growing one in the bioinformatics field. This book provides comprehensive coverage of these topics and will have a long-lasting impact on this evolving subject. Each chapter, written by a leader in the field, introduces state-of-the-art methods to handle information integration, experimental data, and database problems of omics data.

Integrating Omics Data 1st Table of contents:

Chapter 1 – Introduction to Omics Technologies

What Are Omics?

Overview of Key Omics Domains: Genomics, Transcriptomics, Proteomics, Metabolomics

High-Throughput Techniques and Technologies in Omics

Challenges and Opportunities in Omics Research

Chapter 2 – Data Types and Their Characteristics

Genomics: DNA Sequencing and Variants

Transcriptomics: RNA Sequencing and Gene Expression

Proteomics: Protein Identification and Quantification

Metabolomics: Metabolite Profiling and Analysis

Other Omics: Lipidomics, Epigenomics, and More

Chapter 3 – Data Preprocessing and Quality Control

Raw Data Processing: From Raw Files to Usable Data

Normalization and Transformation Methods

Identifying and Handling Missing Data

Quality Control Protocols for Omics Datasets

Chapter 4 – Data Integration Strategies

Early Integration vs. Late Integration

Multi-Omics Integration: Why It Matters

Statistical and Computational Methods for Data Fusion

Challenges of Data Heterogeneity and Scale

Chapter 5 – Multivariate Analysis Techniques

Principal Component Analysis (PCA) and its Applications

Cluster Analysis for Omics Data

Factor Analysis and Independent Component Analysis (ICA)

Network Analysis and System Biology Approaches

Chapter 6 – Machine Learning in Omics Data Integration

Introduction to Machine Learning in Omics Research

Supervised Learning Methods: Classification and Regression

Unsupervised Learning: Clustering and Dimensionality Reduction

Deep Learning Approaches for Omics Data Integration

Chapter 7 – Pathway and Network-Based Approaches

Biological Pathways and Networks in Omics Research

Integrating Omics Data into Biological Pathways

Network Analysis and Construction from Omics Data

Gene Set Enrichment and Functional Annotation

Chapter 8 – Case Studies in Omics Data Integration

Case Study 1: Integrating Genomic and Transcriptomic Data in Cancer Research

Case Study 2: Metabolomics and Proteomics Integration in Disease Modeling

Case Study 3: Multi-Omics Approaches in Microbiome Research

Case Study 4: Integrating Omics for Drug Discovery and Personalized Medicine

Chapter 9 – Applications of Omics Data Integration

Systems Biology and Disease Mechanisms

Precision Medicine and Therapeutic Target Identification

Agricultural and Environmental Applications

Biomarker Discovery and Clinical Applications

Chapter 10 – Future Directions in Omics Data Integration

Advances in Multi-Omics Technologies

Role of Artificial Intelligence and Big Data in Omics

Future Challenges and Opportunities in Data Integration

Ethical Considerations in Omics Research

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Tags: George C Tseng, Debashis Ghosh, Xianghong Jasmine Zhou, Omics Data