Gene Expression Analysis Methods and Protocols /

Gene Expression Analysis Methods and Protocols /

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Bibliographic Details
Corporate Author: SpringerLink (Online service)
Other Authors: Raghavachari, Nalini (Editor), Garcia-Reyero, Natalia (Editor)
Summary:XIV, 366 p. 72 illus., 69 illus. in color.
text
Language:English
Published: New York, NY : Springer US : Imprint: Humana, 2025.
Edition:2nd ed. 2025.
Series:Methods in Molecular Biology, 2880
Subjects:
Biology > Technique.
Gene expression.
Bioinformatics.
Gene Expression Analysis.
Online Access:https://doi.org/10.1007/978-1-0716-4276-4
Format: Electronic Book
Table of Contents:
  • The Salivary Transcriptome: A Window into Local and Systemic Gene Expression Patterns
  • Digital PCR Based Gene Expression Analysis Using a Highly Multiplexed Assay with Universal Detection Probes to Study Induced Pluripotent Stem Cell Differentiation into Cranial Neural Crest Cells
  • Identification of Circular RNA Variants by Oxford Nanopore Long-Read Sequencing
  • Combining Short- and Long-Read Transcriptomes for Targeted Enzyme Discovery
  • Spatial-Omics Methods and Applications
  • Semi-Quantitative Cardiac Specific Gene Expression Validation of the DNA Methylation Microarray in Human Mesenchymal Stem Cells
  • Fusion Transcript Detection from Short-Read RNA-Seq
  • RNA-Seq and Gene Set Enrichment Analysis (GSEA) in Peripheral Blood Mononuclear Cells (PBMCs)
  • Integrating Tissue Microarray to GeoMx® Digital Spatial Profiler Spatial Transcriptomics Assay with Bioinformatics Analysis
  • Establishing a De Novo Annotation of Human Liver Transcriptome Based on Long-Read Direct RNA Sequencing Technology and a Liver-Specific Humanized Mouse Model
  • Exploring Extracellular Vesicle Transcriptomic Diversity through Long-Read Nanopore Sequencing
  • Enhancing Robust and Stable Feature Selection through the Integration of Ranking Methods and Wrapper Techniques in Genetic Data Classification
  • Accelerating Single-Cell Sequencing Data Analysis with SciDAP: A User-Friendly Approach
  • A Selective Review of Network Analysis Methods for Gene Expression Data
  • Deconvolving Bulk Transcriptomics Samples to Obtain Cell Type Proportion Estimates
  • Applying AI/ML for Analyzing Gene Expression Patterns
  • A Machine Learning Pipeline to Screen Large In Vivo Molecular Data to Curate Disease Signatures of High Translational Potential
  • Regulatory Perspectives for Gene Expression-Based Diagnostic Devices.

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