BIG DATA ANALYSIS FOR BIOINFORMATICS AND BIOMEDICAL DISCOVERIES

SHUI QING YE

ISBN 10: 036724022X / ISBN 13: 9780367240226

Published by T&F INDIA, 2025

About this Item

Brand New! Fast Delivery This is an International Edition and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 7-11 days and we do have flat rate for up to 2LB. Extra shipping charges will be requested if the Book weight is more than 5 LB. This Item May be shipped from India,& United states & United Kingdom. Depending on your location and availability.

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Bibliographic Details

• Title: BIG DATA ANALYSIS FOR BIOINFORMATICS AND BIOMEDICAL DISCOVERIES
• Publisher: T&F INDIA
• Publication Date: 2025
• Language: English
• ISBN 10: 036724022X
• ISBN 13: 9780367240226
• Binding: Papeback
• Edition: International Edition
• Condition: Brand New

About this title

Synopsis

Demystifies Biomedical and Biological Big Data AnalysesBig Data Analysis for Bioinformatics and Biomedical Discoveries provides a practical guide to the nuts and bolts of Big Data, enabling you to quickly and effectively harness the power of Big Data to make groundbreaking biological discoveries, carry out translational medical research, and implement personalized genomic medicine. Contributing to the NIH Big Data to Knowledge (BD2K) initiative, the book enhances your computational and quantitative skills so that you can exploit the Big Data being generated in the current omics era.The book explores many significant topics of Big Data analyses in an easily understandable format. It describes popular tools and software for Big Data analyses and explains next-generation DNA sequencing data analyses. It also discusses comprehensive Big Data analyses of several major areas, including the integration of omics data, pharmacogenomics, electronic health record data, and drug discovery.Accessible to biologists, biomedical scientists, bioinformaticians, and computer data analysts, the book keeps complex mathematical deductions and jargon to a minimum. Each chapter includes a theoretical introduction, example applications, data analysis principles, step-by-step tutorials, and authoritative references.