Advances in statistical bioinformatics : models and integrative inference for high-throughput data / edited by Kim-Anh Do, The University of Texas M.D. Anderson Cancer Center, Zhaohui Steven Qin, Emory University, Atlanta, GA, Marina Vannucci, Rice University, Houston, TX.
| Call Number | 572.80285 |
| Title | Advances in statistical bioinformatics : models and integrative inference for high-throughput data / edited by Kim-Anh Do, The University of Texas M.D. Anderson Cancer Center, Zhaohui Steven Qin, Emory University, Atlanta, GA, Marina Vannucci, Rice University, Houston, TX. |
| Physical Description | 1 online resource (xv, 481 pages) : digital, PDF file(s). |
| Notes | Title from publisher's bibliographic system (viewed on 05 Oct 2015). |
| Summary | Providing genome-informed personalized treatment is a goal of modern medicine. Identifying new translational targets in nucleic acid characterizations is an important step toward that goal. The information tsunami produced by such genome-scale investigations is stimulating parallel developments in statistical methodology and inference, analytical frameworks, and computational tools. Within the context of genomic medicine and with a strong focus on cancer research, this book describes the integration of high-throughput bioinformatics data from multiple platforms to inform our understanding of the functional consequences of genomic alterations. This includes rigorous and scalable methods for simultaneously handling diverse data types such as gene expression array, miRNA, copy number, methylation, and next-generation sequencing data. This material is written for statisticians who are interested in modeling and analyzing high-throughput data. Chapters by experts in the field offer a thorough introduction to the biological and technical principles behind multiplatform high-throughput experimentation. |
| Added Author | Do, Kim-Anh, 1960- editor. Qin, Steven, 1972- editor. Vannucci, Marina, 1966- editor. |
| Subject | Bioinformatics Statistical methods. BIOMETRY. Genetics Technique. |
| Multimedia |
Total Ratings:
0
02637nam a22003858i 4500
001
vtls001585346
003
VRT
005
20200921122600.0
006
m|||||o||d||||||||
007
cr||||||||||||
008
200921s2013||||enk o ||1 0|eng|d
020
$a 9781139226448 (ebook)
020
$z 9781107027527 (hardback)
035
$a (UkCbUP)CR9781139226448
039
9
$y 202009211226 $z santha
040
$a UkCbUP $b eng $e rda $c UkCbUP
050
0
0
$a QH324.2 $b .A395 2013
082
0
0
$a 572.80285 $2 23
245
0
0
$a Advances in statistical bioinformatics : $b models and integrative inference for high-throughput data / $c edited by Kim-Anh Do, The University of Texas M.D. Anderson Cancer Center, Zhaohui Steven Qin, Emory University, Atlanta, GA, Marina Vannucci, Rice University, Houston, TX.
264
1
$a Cambridge : $b Cambridge University Press, $c 2013.
300
$a 1 online resource (xv, 481 pages) : $b digital, PDF file(s).
336
$a text $b txt $2 rdacontent
337
$a computer $b c $2 rdamedia
338
$a online resource $b cr $2 rdacarrier
500
$a Title from publisher's bibliographic system (viewed on 05 Oct 2015).
520
$a Providing genome-informed personalized treatment is a goal of modern medicine. Identifying new translational targets in nucleic acid characterizations is an important step toward that goal. The information tsunami produced by such genome-scale investigations is stimulating parallel developments in statistical methodology and inference, analytical frameworks, and computational tools. Within the context of genomic medicine and with a strong focus on cancer research, this book describes the integration of high-throughput bioinformatics data from multiple platforms to inform our understanding of the functional consequences of genomic alterations. This includes rigorous and scalable methods for simultaneously handling diverse data types such as gene expression array, miRNA, copy number, methylation, and next-generation sequencing data. This material is written for statisticians who are interested in modeling and analyzing high-throughput data. Chapters by experts in the field offer a thorough introduction to the biological and technical principles behind multiplatform high-throughput experimentation.
650
0
$a Bioinformatics $x Statistical methods.
650
0
$a BIOMETRY.
650
0
$a Genetics $x Technique.
700
1
$a Do, Kim-Anh, $d 1960- $e editor.
700
1
$a Qin, Steven, $d 1972- $e editor.
700
1
$a Vannucci, Marina, $d 1966- $e editor.
776
0
8
$i Print version: $z 9781107027527
856
4
0
$u https://doi.org/10.1017/CBO9781139226448
999
$a VIRTUA
No Reviews to Display
| Summary | Providing genome-informed personalized treatment is a goal of modern medicine. Identifying new translational targets in nucleic acid characterizations is an important step toward that goal. The information tsunami produced by such genome-scale investigations is stimulating parallel developments in statistical methodology and inference, analytical frameworks, and computational tools. Within the context of genomic medicine and with a strong focus on cancer research, this book describes the integration of high-throughput bioinformatics data from multiple platforms to inform our understanding of the functional consequences of genomic alterations. This includes rigorous and scalable methods for simultaneously handling diverse data types such as gene expression array, miRNA, copy number, methylation, and next-generation sequencing data. This material is written for statisticians who are interested in modeling and analyzing high-throughput data. Chapters by experts in the field offer a thorough introduction to the biological and technical principles behind multiplatform high-throughput experimentation. |
| Notes | Title from publisher's bibliographic system (viewed on 05 Oct 2015). |
| Subject | Bioinformatics Statistical methods. BIOMETRY. Genetics Technique. |
| Multimedia |