Application of Bioinformatics in Cancers
This collection of 25 research papers comprised of 22 original articles and 3 reviews is brought together from international leaders in bioinformatics and biostatistics. The collection highlights recent computational advances that improve the ability to analyze highly complex data sets to identify f...
Saved in:
: | |
---|---|
Year of Publication: | 2019 |
Language: | English |
Physical Description: | 1 electronic resource (418 p.) |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
993548170204498 |
---|---|
ctrlnum |
(CKB)4100000010106283 (oapen)https://directory.doabooks.org/handle/20.500.12854/41042 (EXLCZ)994100000010106283 |
collection |
bib_alma |
record_format |
marc |
spelling |
Brenner, J. Chad auth Application of Bioinformatics in Cancers MDPI - Multidisciplinary Digital Publishing Institute 2019 1 electronic resource (418 p.) text txt rdacontent computer c rdamedia online resource cr rdacarrier Open access Unrestricted online access star This collection of 25 research papers comprised of 22 original articles and 3 reviews is brought together from international leaders in bioinformatics and biostatistics. The collection highlights recent computational advances that improve the ability to analyze highly complex data sets to identify factors critical to cancer biology. Novel deep learning algorithms represent an emerging and highly valuable approach for collecting, characterizing and predicting clinical outcomes data. The collection highlights several of these approaches that are likely to become the foundation of research and clinical practice in the future. In fact, many of these technologies reveal new insights about basic cancer mechanisms by integrating data sets and structures that were previously immiscible. English cancer treatment extreme learning independent prognostic power AID/APOBEC HP gene inactivation biomarkers biomarker discovery chemotherapy artificial intelligence epigenetics comorbidity score denoising autoencoders protein single-biomarkers gene signature extraction high-throughput analysis concatenated deep feature feature selection differential gene expression analysis colorectal cancer ovarian cancer multiple-biomarkers gefitinib cancer biomarkers classification cancer biomarker mutation hierarchical clustering analysis HNSCC cell-free DNA network analysis drug resistance hTERT variable selection KRAS mutation single-cell sequencing network target skin cutaneous melanoma telomeres Neoantigen Prediction datasets clinical/environmental factors StAR PD-L1 miRNA circulating tumor DNA (ctDNA) false discovery rate predictive model Computational Immunology brain metastases observed survival interval next generation sequencing brain machine learning cancer prognosis copy number aberration mutable motif steroidogenic enzymes tumor mortality tumor microenvironment somatic mutation transcriptional signatures omics profiles mitochondrial metabolism Bufadienolide-like chemicals cancer-related pathways intratumor heterogeneity estrogen locoregionally advanced RNA feature extraction and interpretation treatment de-escalation activation induced deaminase knockoffs R package copy number variation gene loss biomarkers cancer CRISPR overall survival histopathological imaging self-organizing map Network Analysis oral cancer biostatistics firehose Bioinformatics tool alternative splicing biomarkers diseases genes histopathological imaging features imaging TCGA decision support systems The Cancer Genome Atlas molecular subtypes molecular mechanism omics curative surgery network pharmacology methylation bioinformatics neurological disorders precision medicine cancer modeling miRNAs breast cancer detection functional analysis biomarker signature anti-cancer hormone sensitive cancers deep learning DNA sequence profile pancreatic cancer telomerase Monte Carlo mixture of normal distributions survival analysis tumor infiltrating lymphocytes curation pathophysiology GEO DataSets head and neck cancer gene expression analysis erlotinib meta-analysis traditional Chinese medicine breast cancer TCGA mining breast cancer prognosis microarray DNA interaction health strengthening herb cancer genomic instability 3-03921-788-7 |
language |
English |
format |
eBook |
author |
Brenner, J. Chad |
spellingShingle |
Brenner, J. Chad Application of Bioinformatics in Cancers |
author_facet |
Brenner, J. Chad |
author_variant |
j c b jc jcb |
author_sort |
Brenner, J. Chad |
title |
Application of Bioinformatics in Cancers |
title_full |
Application of Bioinformatics in Cancers |
title_fullStr |
Application of Bioinformatics in Cancers |
title_full_unstemmed |
Application of Bioinformatics in Cancers |
title_auth |
Application of Bioinformatics in Cancers |
title_new |
Application of Bioinformatics in Cancers |
title_sort |
application of bioinformatics in cancers |
publisher |
MDPI - Multidisciplinary Digital Publishing Institute |
publishDate |
2019 |
physical |
1 electronic resource (418 p.) |
isbn |
3-03921-789-5 3-03921-788-7 |
illustrated |
Not Illustrated |
work_keys_str_mv |
AT brennerjchad applicationofbioinformaticsincancers |
status_str |
n |
ids_txt_mv |
(CKB)4100000010106283 (oapen)https://directory.doabooks.org/handle/20.500.12854/41042 (EXLCZ)994100000010106283 |
carrierType_str_mv |
cr |
is_hierarchy_title |
Application of Bioinformatics in Cancers |
_version_ |
1796648759650680836 |
fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>06249nam-a2201909z--4500</leader><controlfield tag="001">993548170204498</controlfield><controlfield tag="005">20240130170919.0</controlfield><controlfield tag="006">m o d </controlfield><controlfield tag="007">cr|mn|---annan</controlfield><controlfield tag="008">202102s2019 xx |||||o ||| 0|eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">3-03921-789-5</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(CKB)4100000010106283</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(oapen)https://directory.doabooks.org/handle/20.500.12854/41042</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(EXLCZ)994100000010106283</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Brenner, J. Chad</subfield><subfield code="4">auth</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Application of Bioinformatics in Cancers</subfield></datafield><datafield tag="260" ind1=" " ind2=" "><subfield code="b">MDPI - Multidisciplinary Digital Publishing Institute</subfield><subfield code="c">2019</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 electronic resource (418 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="506" ind1=" " ind2=" "><subfield code="a">Open access</subfield><subfield code="f">Unrestricted online access</subfield><subfield code="2">star</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">This collection of 25 research papers comprised of 22 original articles and 3 reviews is brought together from international leaders in bioinformatics and biostatistics. The collection highlights recent computational advances that improve the ability to analyze highly complex data sets to identify factors critical to cancer biology. Novel deep learning algorithms represent an emerging and highly valuable approach for collecting, characterizing and predicting clinical outcomes data. The collection highlights several of these approaches that are likely to become the foundation of research and clinical practice in the future. In fact, many of these technologies reveal new insights about basic cancer mechanisms by integrating data sets and structures that were previously immiscible.</subfield></datafield><datafield tag="546" ind1=" " ind2=" "><subfield code="a">English</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">cancer treatment</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">extreme learning</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">independent prognostic power</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">AID/APOBEC</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">HP</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">gene inactivation biomarkers</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">biomarker discovery</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">chemotherapy</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">artificial intelligence</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">epigenetics</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">comorbidity score</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">denoising autoencoders</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">protein</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">single-biomarkers</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">gene signature extraction</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">high-throughput analysis</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">concatenated deep feature</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">feature selection</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">differential gene expression analysis</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">colorectal cancer</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">ovarian cancer</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">multiple-biomarkers</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">gefitinib</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">cancer biomarkers</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">classification</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">cancer biomarker</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">mutation</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">hierarchical clustering analysis</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">HNSCC</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">cell-free DNA</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">network analysis</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">drug resistance</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">hTERT</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">variable selection</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">KRAS mutation</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">single-cell sequencing</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">network target</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">skin cutaneous melanoma</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">telomeres</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Neoantigen Prediction</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">datasets</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">clinical/environmental factors</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">StAR</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">PD-L1</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">miRNA</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">circulating tumor DNA (ctDNA)</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">false discovery rate</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">predictive model</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Computational Immunology</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">brain metastases</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">observed survival interval</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">next generation sequencing</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">brain</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">machine learning</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">cancer prognosis</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">copy number aberration</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">mutable motif</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">steroidogenic enzymes</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">tumor</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">mortality</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">tumor microenvironment</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">somatic mutation</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">transcriptional signatures</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">omics profiles</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">mitochondrial metabolism</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Bufadienolide-like chemicals</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">cancer-related pathways</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">intratumor heterogeneity</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">estrogen</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">locoregionally advanced</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">RNA</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">feature extraction and interpretation</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">treatment de-escalation</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">activation induced deaminase</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">knockoffs</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">R package</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">copy number variation</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">gene loss biomarkers</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">cancer CRISPR</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">overall survival</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">histopathological imaging</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">self-organizing map</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Network Analysis</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">oral cancer</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">biostatistics</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">firehose</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Bioinformatics tool</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">alternative splicing</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">biomarkers</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">diseases genes</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">histopathological imaging features</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">imaging</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">TCGA</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">decision support systems</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">The Cancer Genome Atlas</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">molecular subtypes</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">molecular mechanism</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">omics</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">curative surgery</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">network pharmacology</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">methylation</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">bioinformatics</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">neurological disorders</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">precision medicine</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">cancer modeling</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">miRNAs</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">breast cancer detection</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">functional analysis</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">biomarker signature</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">anti-cancer</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">hormone sensitive cancers</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">deep learning</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">DNA sequence profile</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">pancreatic cancer</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">telomerase</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Monte Carlo</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">mixture of normal distributions</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">survival analysis</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">tumor infiltrating lymphocytes</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">curation</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">pathophysiology</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">GEO DataSets</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">head and neck cancer</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">gene expression analysis</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">erlotinib</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">meta-analysis</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">traditional Chinese medicine</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">breast cancer</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">TCGA mining</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">breast cancer prognosis</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">microarray</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">DNA</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">interaction</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">health strengthening herb</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">cancer</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">genomic instability</subfield></datafield><datafield tag="776" ind1=" " ind2=" "><subfield code="z">3-03921-788-7</subfield></datafield><datafield tag="906" ind1=" " ind2=" "><subfield code="a">BOOK</subfield></datafield><datafield tag="ADM" ind1=" " ind2=" "><subfield code="b">2024-02-02 00:38:43 Europe/Vienna</subfield><subfield code="f">system</subfield><subfield code="c">marc21</subfield><subfield code="a">2020-02-01 22:26:53 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=5338743650004498&Force_direct=true</subfield><subfield code="Z">5338743650004498</subfield><subfield code="b">Available</subfield><subfield code="8">5338743650004498</subfield></datafield></record></collection> |