高雄醫學大學圖書館 |

Big data analytics in HIV/AIDS research

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Big data analytics in HIV/AIDS research/ Ali Al Mazari, editor.
other author:	Al Mazari, Ali,
Published:	Hershey, Pennsylvania :IGI Global, : [2018],
Description:	1 online resource (xxix, 294 p.)
基督教聖經之智慧書導讀 :	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.
Subject:	HIV infections - Treatment. -
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:

263644
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.
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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 $2 96060 $a HIV infections $x Treatment. $3 263644
650 0 $a Big data. $3 387512
650 0 $2 96060 $a Data mining. $3 255525
650 0 $a Datasets as Topic $3 433524
650 0 $2 96060 $a HIV Infections $x epidemiology $3 224283
650 0 $a Data Mining $3 433525
700 1 $a Al Mazari, Ali, $d 1971- $e editor. $3 433523
856 4 0 $u http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-5225-3203-3