Statistical Methods for the Analysis of Genomic Data
In recent years, technological breakthroughs have greatly enhanced our ability to understand the complex world of molecular biology. Rapid developments in genomic profiling techniques, such as high-throughput sequencing, have brought new opportunities and challenges to the fields of computational bi...
Saved in:
HerausgeberIn: | |
---|---|
Sonstige: | |
Year of Publication: | 2020 |
Language: | English |
Physical Description: | 1 electronic resource (136 p.) |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
993546379804498 |
---|---|
ctrlnum |
(CKB)5400000000044149 (oapen)https://directory.doabooks.org/handle/20.500.12854/68899 (EXLCZ)995400000000044149 |
collection |
bib_alma |
record_format |
marc |
spelling |
Jiang, Hui edt Statistical Methods for the Analysis of Genomic Data Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2020 1 electronic resource (136 p.) text txt rdacontent computer c rdamedia online resource cr rdacarrier In recent years, technological breakthroughs have greatly enhanced our ability to understand the complex world of molecular biology. Rapid developments in genomic profiling techniques, such as high-throughput sequencing, have brought new opportunities and challenges to the fields of computational biology and bioinformatics. Furthermore, by combining genomic profiling techniques with other experimental techniques, many powerful approaches (e.g., RNA-Seq, Chips-Seq, single-cell assays, and Hi-C) have been developed in order to help explore complex biological systems. As a result of the increasing availability of genomic datasets, in terms of both volume and variety, the analysis of such data has become a critical challenge as well as a topic of great interest. Therefore, statistical methods that address the problems associated with these newly developed techniques are in high demand. This book includes a number of studies that highlight the state-of-the-art statistical methods for the analysis of genomic data and explore future directions for improvement. English Research & information: general bicssc Mathematics & science bicssc multiple cancer types integrative analysis omics data prognosis modeling classification gene set enrichment analysis boosting kernel method Bayes factor Bayesian mixed-effect model CpG sites DNA methylation Ordinal responses GEE lipid-environment interaction longitudinal lipidomics study penalized variable selection convolutional neural networks deep learning feed-forward neural networks machine learning gene regulatory network nonparanormal graphical model network substructure false discovery rate control gaussian finite mixture model clustering analysis uncertainty expectation-maximization algorithm classification boundary gene expression RNA-seq 3-03936-140-6 3-03936-141-4 He, Zhi edt Jiang, Hui oth He, Zhi oth |
language |
English |
format |
eBook |
author2 |
He, Zhi Jiang, Hui He, Zhi |
author_facet |
He, Zhi Jiang, Hui He, Zhi |
author2_variant |
h j hj z h zh |
author2_role |
HerausgeberIn Sonstige Sonstige |
title |
Statistical Methods for the Analysis of Genomic Data |
spellingShingle |
Statistical Methods for the Analysis of Genomic Data |
title_full |
Statistical Methods for the Analysis of Genomic Data |
title_fullStr |
Statistical Methods for the Analysis of Genomic Data |
title_full_unstemmed |
Statistical Methods for the Analysis of Genomic Data |
title_auth |
Statistical Methods for the Analysis of Genomic Data |
title_new |
Statistical Methods for the Analysis of Genomic Data |
title_sort |
statistical methods for the analysis of genomic data |
publisher |
MDPI - Multidisciplinary Digital Publishing Institute |
publishDate |
2020 |
physical |
1 electronic resource (136 p.) |
isbn |
3-03936-140-6 3-03936-141-4 |
illustrated |
Not Illustrated |
work_keys_str_mv |
AT jianghui statisticalmethodsfortheanalysisofgenomicdata AT hezhi statisticalmethodsfortheanalysisofgenomicdata |
status_str |
n |
ids_txt_mv |
(CKB)5400000000044149 (oapen)https://directory.doabooks.org/handle/20.500.12854/68899 (EXLCZ)995400000000044149 |
carrierType_str_mv |
cr |
is_hierarchy_title |
Statistical Methods for the Analysis of Genomic Data |
author2_original_writing_str_mv |
noLinkedField noLinkedField noLinkedField |
_version_ |
1787548474594557952 |
fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>03255nam-a2200709z--4500</leader><controlfield tag="001">993546379804498</controlfield><controlfield tag="005">20231214133547.0</controlfield><controlfield tag="006">m o d </controlfield><controlfield tag="007">cr|mn|---annan</controlfield><controlfield tag="008">202105s2020 xx |||||o ||| 0|eng d</controlfield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(CKB)5400000000044149</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(oapen)https://directory.doabooks.org/handle/20.500.12854/68899</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(EXLCZ)995400000000044149</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Jiang, Hui</subfield><subfield code="4">edt</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Statistical Methods for the Analysis of Genomic Data</subfield></datafield><datafield tag="260" ind1=" " ind2=" "><subfield code="a">Basel, Switzerland</subfield><subfield code="b">MDPI - Multidisciplinary Digital Publishing Institute</subfield><subfield code="c">2020</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 electronic resource (136 p.)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">computer</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">online resource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">In recent years, technological breakthroughs have greatly enhanced our ability to understand the complex world of molecular biology. Rapid developments in genomic profiling techniques, such as high-throughput sequencing, have brought new opportunities and challenges to the fields of computational biology and bioinformatics. Furthermore, by combining genomic profiling techniques with other experimental techniques, many powerful approaches (e.g., RNA-Seq, Chips-Seq, single-cell assays, and Hi-C) have been developed in order to help explore complex biological systems. As a result of the increasing availability of genomic datasets, in terms of both volume and variety, the analysis of such data has become a critical challenge as well as a topic of great interest. Therefore, statistical methods that address the problems associated with these newly developed techniques are in high demand. This book includes a number of studies that highlight the state-of-the-art statistical methods for the analysis of genomic data and explore future directions for improvement.</subfield></datafield><datafield tag="546" ind1=" " ind2=" "><subfield code="a">English</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Research & information: general</subfield><subfield code="2">bicssc</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Mathematics & science</subfield><subfield code="2">bicssc</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">multiple cancer types</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">integrative analysis</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">omics data</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">prognosis modeling</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">classification</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">gene set enrichment analysis</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">boosting</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">kernel method</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Bayes factor</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Bayesian mixed-effect model</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">CpG sites</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">DNA methylation</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Ordinal responses</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">GEE</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">lipid-environment interaction</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">longitudinal lipidomics study</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">penalized variable selection</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">convolutional neural networks</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">deep learning</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">feed-forward neural networks</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">machine learning</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">gene regulatory network</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">nonparanormal graphical model</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">network substructure</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">false discovery rate control</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">gaussian finite mixture model</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">clustering analysis</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">uncertainty</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">expectation-maximization algorithm</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">classification boundary</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">gene expression</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">RNA-seq</subfield></datafield><datafield tag="776" ind1=" " ind2=" "><subfield code="z">3-03936-140-6</subfield></datafield><datafield tag="776" ind1=" " ind2=" "><subfield code="z">3-03936-141-4</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">He, Zhi</subfield><subfield code="4">edt</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Jiang, Hui</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">He, Zhi</subfield><subfield code="4">oth</subfield></datafield><datafield tag="906" ind1=" " ind2=" "><subfield code="a">BOOK</subfield></datafield><datafield tag="ADM" ind1=" " ind2=" "><subfield code="b">2023-12-15 05:57:44 Europe/Vienna</subfield><subfield code="f">system</subfield><subfield code="c">marc21</subfield><subfield code="a">2022-04-04 09:22:53 Europe/Vienna</subfield><subfield code="g">false</subfield></datafield><datafield tag="AVE" ind1=" " ind2=" "><subfield code="i">DOAB Directory of Open Access Books</subfield><subfield code="P">DOAB Directory of Open Access Books</subfield><subfield code="x">https://eu02.alma.exlibrisgroup.com/view/uresolver/43ACC_OEAW/openurl?u.ignore_date_coverage=true&portfolio_pid=5338226500004498&Force_direct=true</subfield><subfield code="Z">5338226500004498</subfield><subfield code="b">Available</subfield><subfield code="8">5338226500004498</subfield></datafield></record></collection> |