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Big data analytics in HIV/AIDS research

Al Mazari, Ali, (1971-)

Big data analytics in HIV/AIDS research

Record Type:	Electronic resources : Monograph/item
Title/Author:	Big data analytics in HIV/AIDS researchAli Al Mazari, editor.
other author:	Al Mazari, Ali,
Published:	Hershey, Pennsylvania :IGI Global,[2018]
Description:	1 online resource (xxix, 294 p.)
Subject:	HIV infectionsTreatment.
Online resource:	http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-5225-3203-3
ISBN:	9781522532040 (ebook)

Big data analytics in HIV/AIDS research
Big data analytics in HIV/AIDS research[electronic resource] /Ali Al Mazari, editor. - Hershey, Pennsylvania :IGI Global,[2018] - 1 online resource (xxix, 294 p.)

Includes bibliographical references and index.

Chapter 1. Computational analysis of reverse transcriptase resistance to inhibitors in HIV-1 -- Chapter 2. Statistical and computational needs for big data challenges -- Chapter 3. Usage of big data prediction techniques for predictive analysis in HIV/AIDS -- Chapter 4. Computational and data mining perspectives on HIV/AIDS in big data era: opportunities, challenges, and future directions -- Chapter 5. Risks, security, and privacy for HIV/AIDS data: big data perspective -- Chapter 6. Prevalence in MSM is enhanced by role versatility -- Chapter 7. Dissection of HIV-1 protease subtype B inhibitors resistance through molecular modeling approaches: resistance to protease inhibitors -- Chapter 8. HIV-associated neurocognitive disorder: the interaction between HIV-1 and dopamine transporter structure.

Restricted to subscribers or individual electronic text purchasers.

This book provides emerging research on the development and implementation of computational techniques in big data analysis for biological and medical practices. While highlighting topics such as deep learning, management software, and molecular modeling, this publication explores the various applications of data analysis in clinical decision making.

ISBN: 9781522532040 (ebook)Subjects--Topical Terms:

318169
HIV infections
--Treatment.

LC Class. No.: RA643.8 / .B54 2018e

Dewey Class. No.: 614.5/993920072

National Library of Medicine Call No.: WC 503.41 / .B54 2018e

Big data analytics in HIV/AIDS research
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245 0 0 $a Big data analytics in HIV/AIDS research $h [electronic resource] / $c Ali Al Mazari, editor.
260 $a Hershey, Pennsylvania : $b IGI Global, $c [2018]
300 $a 1 online resource (xxix, 294 p.)
504 $a Includes bibliographical references and index.
505 0 $a Chapter 1. Computational analysis of reverse transcriptase resistance to inhibitors in HIV-1 -- Chapter 2. Statistical and computational needs for big data challenges -- Chapter 3. Usage of big data prediction techniques for predictive analysis in HIV/AIDS -- Chapter 4. Computational and data mining perspectives on HIV/AIDS in big data era: opportunities, challenges, and future directions -- Chapter 5. Risks, security, and privacy for HIV/AIDS data: big data perspective -- Chapter 6. Prevalence in MSM is enhanced by role versatility -- Chapter 7. Dissection of HIV-1 protease subtype B inhibitors resistance through molecular modeling approaches: resistance to protease inhibitors -- Chapter 8. HIV-associated neurocognitive disorder: the interaction between HIV-1 and dopamine transporter structure.
506 $a Restricted to subscribers or individual electronic text purchasers.
520 $a This book provides emerging research on the development and implementation of computational techniques in big data analysis for biological and medical practices. While highlighting topics such as deep learning, management software, and molecular modeling, this publication explores the various applications of data analysis in clinical decision making.
650 0 $a HIV infections $x Treatment. $3 318169
650 0 $a Big data. $3 609582
650 0 $a Data mining. $3 184440
650 0 $a Datasets as Topic $3 807977
650 0 $a HIV Infections $x epidemiology $3 807978
650 0 $a Data Mining $3 807979
700 1 $a Al Mazari, Ali, $d 1971- $e editor. $3 807976
856 4 0 $u http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-5225-3203-3