Translational Bioinformatics for Therapeutic Development

Herausgegeben:Markowitz, Joseph

• Gebundenes Buch

Produktbeschreibung

This volume introduces Translational Bioinformatics as it relates to therapeutic development, and addresses the techniques needed to effectively translate large data sets to relevant biological networks. Chapters detail clinical informatics infrastructure, and leverage pathology, immunology, pharmacology, genomic, proteomic, and metabolomic informatics approaches. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, application details for both the expert and non-expert reader, and tips on troubleshooting and avoiding known pitfalls.

Authoritative and practical, Translational Bioinformatics for Therapeutic Development: Methods and Protocols aims to ensure success in the study of Translational Bioinformatics.

Produktdetails

• Methods in Molecular Biology 2194
• Verlag: Humana / Springer US / Springer, Berlin
• Artikelnr. des Verlages: 978-1-0716-0848-7
• 1st ed. 2021
• Seitenzahl: 336
• Erscheinungstermin: 15. September 2020
• Englisch
• Abmessung: 260mm x 183mm x 24mm
• Gewicht: 810g
• ISBN-13: 9781071608487
• ISBN-10: 1071608487
• Artikelnr.: 59305800

Inhaltsangabe

Development and Optimization of Clinical Informatics Infrastructure to Support Bioinformatics at an Oncology Center.- Leveraging Pathology Informatics Concepts to Achieve Discrete Lab Data for Clinical Use and Translational Research.- Cohort Identification for Translational Bioinformatics Studies.- Transitioning Clinical Practice Guidelines into the Electronic Health Record through Clinical Pathways.- Variable Selection for Time-to-Event Data.- Binary Classification for Failure Risk Assessment.- Challenges and Opportunities of Genomic Approaches in Therapeutics Development.- Accessible Pipeline for Translational Research using TCGA: Examples of Relating Gene Mechanism to Disease Specific Outcomes.- Statistical and Bioinformatics Analysis of Data from Bulk and Single-Cell RNA Sequencing Experiments.- Investigating Inter- and Intra-Sample Diversity of Single-Cell RNA Sequencing Datasets.- Managing a Large-scale Multi-Omics Project: A Team Science Case Study in Proteogenomics.- Synergistic Drug Combination Prediction by Integrating Multi-omics Data in Deep Learning Models.- Introduction to Multi-Parametric Flow Cytometry and Analysis of High-Dimensional Data.- High Dimensional Flow Cytometry Analysis of Regulatory Receptors on Human T cells, NK cells, and NKT Cells*.- Quantitative Analysis of Bile Acid with UHPLC-MS/MS.- Sample Preparation and Data Analysis for NMR-based Metabolomics.