東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁 到查詢結果 [ subject:"HIV infections- Treatment." ]

切換: 標籤 | MARC模式 | ISBD

Big data analytics in HIV/AIDS research

Al Mazari, Ali, (1971-)

FindBook

Google Book

Amazon

博客來

Big data analytics in HIV/AIDS research

紀錄類型:	書目-電子資源 : Monograph/item
正題名/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.)
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.
標題:	HIV infections - Treatment. -
電子資源:	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:

929598
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
LDR:02152nmm a2200289 a 4500 001 2137921
003 IGIG
005 20181029091933.0
006 m o d
007 cr cn
008 181117s2018 pau fob 001 0 eng d
010 $z 2017022903
020 $a 9781522532040 (ebook)
020 $a 9781522532033 (hardcover)
035 $a (OCoLC)1029775026
035 $a 1071025291
040 $a CaBNVSL $b eng $c CaBNVSL $d CaBNVSL
050 4 $a RA643.8 $b .B54 2018e
060 1 0 $a WC 503.41 $b .B54 2018e
082 0 4 $a 614.5/993920072 $2 23
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 929598
650 0 $a Big data. $3 2045508
650 0 $a Data mining. $3 562972
650 0 $a Datasets as Topic $3 3310922
650 0 $a HIV Infections $x epidemiology $3 3310923
650 0 $a Data Mining $3 3310924
700 1 $a Al Mazari, Ali, $d 1971- $e editor. $3 3310921
856 4 0 $u http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-5225-3203-3