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Big data analytics in HIV/AIDS resea...

Al Mazari, Ali, (1971-)

Big data analytics in HIV/AIDS research[electronic resource] /

紀錄類型:	書目-電子資源 : Monograph/item
杜威分類號:	614.5/993920072
書名/作者:	Big data analytics in HIV/AIDS research/ Ali Al Mazari, editor.
其他作者:	Al Mazari, Ali,
出版者:	Hershey, Pennsylvania : : IGI Global,, [2018]
面頁冊數:	1 online resource (xxix, 294 p.)
標題:	HIV infections - Treatment.
標題:	Big data.
標題:	Data mining.
標題:	Datasets as Topic
標題:	HIV Infections - epidemiology
標題:	Data Mining
ISBN:	9781522532040 (ebook)
ISBN:	9781522532033 (hardcover)
書目註:	Includes bibliographical references and index.
內容註:	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.
摘要、提要註:	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.
電子資源:	http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-5225-3203-3

Big data analytics in HIV/AIDS research[electronic resource] /
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:

406343
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[electronic resource] /
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020 $a 9781522532033 (hardcover)
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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 406343
650 0 $a Big data. $3 571002
650 0 $a Data mining. $3 337740
650 0 $a Datasets as Topic $3 733097
650 0 $a HIV Infections $x epidemiology $3 733098
650 0 $a Data Mining $3 733099
700 1 $a Al Mazari, Ali, $d 1971- $e editor. $3 733096
856 4 0 $u http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-5225-3203-3

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