A DEEP LEARNING APPROACH FOR CANCER DETECTION AND RELEVANT GENE IDENTIFICATION

Cancer detection from gene expression data continues to pose a challenge due to the high dimensionality and complexity of these data. After decades of research there is still uncertainty in the clinical diagnosis of cancer and the identification of tumor-specific markers. Here we present a deep lear...

Full description

Saved in:
Bibliographic Details
Published in:Biocomputing 2017 Vol. 22; pp. 219 - 229
Main Authors: DANAEE, PADIDEH, GHAEINI, REZA, HENDRIX, DAVID A.
Format: Book Chapter Journal Article
Language:English
Published: United States WORLD SCIENTIFIC 01.01.2017
Subjects:
Algorithms
Biomarkers, Tumor - genetics
Breast Neoplasms - classification
Breast Neoplasms - diagnosis
Breast Neoplasms - genetics
Computational Biology
Databases, Genetic - statistics & numerical data
Female
Gene Expression Profiling - statistics & numerical data
Gene Ontology
Humans
PATTERNS IN BIOMEDICAL DATA-HOW DO WE FIND THEM?
Principal Component Analysis
Supervised Machine Learning
Stacked Denoising Autoencoder
Dimensionality Reduction
Cancer Detection
RNA-seq Expression
Deep Learning
Classification
ISBN:9789813207820, 9789813207806, 9813207825, 9813207809, 9789813207813, 9813207817
ISSN:2335-6936
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Abstract Cancer detection from gene expression data continues to pose a challenge due to the high dimensionality and complexity of these data. After decades of research there is still uncertainty in the clinical diagnosis of cancer and the identification of tumor-specific markers. Here we present a deep learning approach to cancer detection, and to the identification of genes critical for the diagnosis of breast cancer. First, we used Stacked Denoising Autoencoder (SDAE) to deeply extract functional features from high dimensional gene expression profiles. Next, we evaluated the performance of the extracted representation through supervised classification models to verify the usefulness of the new features in cancer detection. Lastly, we identified a set of highly interactive genes by analyzing the SDAE connectivity matrices. Our results and analysis illustrate that these highly interactive genes could be useful cancer biomarkers for the detection of breast cancer that deserve further studies.
AbstractList Cancer detection from gene expression data continues to pose a challenge due to the high dimensionality and complexity of these data. After decades of research there is still uncertainty in the clinical diagnosis of cancer and the identification of tumor-specific markers. Here we present a deep learning approach to cancer detection, and to the identification of genes critical for the diagnosis of breast cancer. First, we used Stacked Denoising Autoencoder (SDAE) to deeply extract functional features from high dimensional gene expression profiles. Next, we evaluated the performance of the extracted representation through supervised classification models to verify the usefulness of the new features in cancer detection. Lastly, we identified a set of highly interactive genes by analyzing the SDAE connectivity matrices. Our results and analysis illustrate that these highly interactive genes could be useful cancer biomarkers for the detection of breast cancer that deserve further studies.
Author DANAEE, PADIDEH
GHAEINI, REZA
HENDRIX, DAVID A.
Author_xml – sequence: 1
  givenname: PADIDEH
  surname: DANAEE
  fullname: DANAEE, PADIDEH
  email: danaeep@oregonstate.edu
– sequence: 2
  givenname: REZA
  surname: GHAEINI
  fullname: GHAEINI, REZA
  email: ghaeinim@oregonstate.edu
– sequence: 3
  givenname: DAVID A.
  surname: HENDRIX
  fullname: HENDRIX, DAVID A.
  email: david.hendrix@oregonstate.edu
BackLink https://www.ncbi.nlm.nih.gov/pubmed/27896977$$D View this record in MEDLINE/PubMed
BookMark eNqdkUtLw0AUhccXttb-ARcySzfReSQzmYWLkE5rIExKiG6HPCYQTZuatIj_3imtILpzcy_c850L954rcL7u1gaAG4zuMXbJg-C-8DEliNuqESLkBEx_DAk6BWNCqecwQdnZL20EpsPwiqwNc88l7BKMiNWZ4HwMVABnUi5hLINURWoBg-UyTYLwCc6TFIaBCmVqiUyGWZQoGKgZTGUsXwKVwYVUEkYzqbJoHoXBHrgGF3XeDmZ67BPwPJdZ-OTEycIisbNxMakdj3oVK6ifs5L7LmeVj92cGbckphKV4VwQ5DJUIlzZA2jJRc1oIYzwauIXFNMJuDvs3fTd-84MW71qhtK0bb423W7Q2Hetn3qMWPT2iO6Klan0pm9Wef-pv39gAX4APrq-rYayMettUzelLrruza5Ceh-C_huCdT7-z6mLvjE1_QL8jn_D
ContentType Book Chapter
Journal Article
Copyright World Scientific Publishing Co. Pte. Ltd.
Copyright_xml – notice: World Scientific Publishing Co. Pte. Ltd.
DBID CGR
CUY
CVF
ECM
EIF
NPM
7X8
DOI 10.1142/9789813207813_0022
DatabaseName Medline
MEDLINE
MEDLINE (Ovid)
MEDLINE
MEDLINE
PubMed
MEDLINE - Academic
DatabaseTitle MEDLINE
Medline Complete
MEDLINE with Full Text
PubMed
MEDLINE (Ovid)
MEDLINE - Academic
DatabaseTitleList MEDLINE
MEDLINE - Academic
Database_xml – sequence: 1
  dbid: NPM
  name: PubMed
  url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed
  sourceTypes: Index Database
– sequence: 2
  dbid: 7X8
  name: MEDLINE - Academic
  url: https://search.proquest.com/medline
  sourceTypes: Aggregation Database
DeliveryMethod fulltext_linktorsrc
EISBN 9789813207820
9813207825
9789813207813
9813207817
EISSN 2335-6936
Editor Altman, Russ B
Murray, Tiffany A
Hunter, Lawrence
Ritchie, Marylyn D
Klein, Teri E
Dunker, A Keith
Editor_xml – sequence: 1
  givenname: Russ B
  surname: Altman
  fullname: Altman, Russ B
  organization: Stanford
– sequence: 2
  givenname: A Keith
  surname: Dunker
  fullname: Dunker, A Keith
  organization: Indiana University
– sequence: 3
  givenname: Lawrence
  surname: Hunter
  fullname: Hunter, Lawrence
  organization: University of Colorado Health Sciences Center
– sequence: 4
  givenname: Marylyn D
  surname: Ritchie
  fullname: Ritchie, Marylyn D
  organization: The Pennsylvania State University
– sequence: 5
  givenname: Tiffany A
  surname: Murray
  fullname: Murray, Tiffany A
  organization: Stanford
– sequence: 6
  givenname: Teri E
  surname: Klein
  fullname: Klein, Teri E
  organization: Stanford
EndPage 229
ExternalDocumentID 27896977
10.1142/9789813207813_0022
Genre Journal Article
GrantInformation_xml – fundername: NIA NIH HHS
  grantid: R21 AG052950
GroupedDBID 9WS
AABBV
AATMT
ACZWY
ADCHV
AIQUZ
ALMA_UNASSIGNED_HOLDINGS
BBABE
CZZ
V1H
CGR
CUY
CVF
ECM
EIF
NPM
7X8
ID FETCH-LOGICAL-p412f-535d6b38a6c78476d814a6e4c2ed9de77920460c01d8203c79f63b9e95f28b313
ISBN 9789813207820
9789813207806
9813207825
9813207809
9789813207813
9813207817
ISICitedReferencesCount 128
ISICitedReferencesURI http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000391254200022&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
IngestDate Sun Nov 09 14:11:48 EST 2025
Thu Jan 02 22:44:02 EST 2025
Sat Mar 15 05:01:06 EDT 2025
IsDoiOpenAccess false
IsOpenAccess true
IsPeerReviewed true
IsScholarly true
Keywords Stacked Denoising Autoencoder
Dimensionality Reduction
Cancer Detection
RNA-seq Expression
Deep Learning
Classification
Language English
LinkModel OpenURL
MeetingName Pacific Symposium on Biocomputing 2017
MergedId FETCHMERGED-LOGICAL-p412f-535d6b38a6c78476d814a6e4c2ed9de77920460c01d8203c79f63b9e95f28b313
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
OpenAccessLink https://doi.org/10.1142/9789813207813_0022
PMID 27896977
PQID 1844603562
PQPubID 23479
PageCount 11
ParticipantIDs pubmed_primary_27896977
proquest_miscellaneous_1844603562
worldscientific_books_10_1142_9789813207813_0022
worldscientific_books_10_1142_9789813207813_0022_brief
PublicationCentury 2000
PublicationDate 20170100
PublicationDateYYYYMMDD 2017-01-01
PublicationDate_xml – month: 01
  year: 2017
  text: 20170100
PublicationDecade 2010
PublicationPlace United States
PublicationPlace_xml – name: United States
PublicationSubtitle Proceedings of the Pacific Symposium
PublicationTitle Biocomputing 2017
PublicationTitleAlternate Pac Symp Biocomput
PublicationYear 2017
Publisher WORLD SCIENTIFIC
Publisher_xml – name: WORLD SCIENTIFIC
SSID ssj0002175426
ssib015896917
Score 2.5822124
Snippet Cancer detection from gene expression data continues to pose a challenge due to the high dimensionality and complexity of these data. After decades of research...
SourceID proquest
pubmed
worldscientific
SourceType Aggregation Database
Index Database
Enrichment Source
Publisher
StartPage 219
SubjectTerms Algorithms
Biomarkers, Tumor - genetics
Breast Neoplasms - classification
Breast Neoplasms - diagnosis
Breast Neoplasms - genetics
Computational Biology
Databases, Genetic - statistics & numerical data
Female
Gene Expression Profiling - statistics & numerical data
Gene Ontology
Humans
PATTERNS IN BIOMEDICAL DATA-HOW DO WE FIND THEM?
Principal Component Analysis
Supervised Machine Learning
Title A DEEP LEARNING APPROACH FOR CANCER DETECTION AND RELEVANT GENE IDENTIFICATION
URI https://www.worldscientific.com/doi/10.1142/9789813207813_0022
https://www.ncbi.nlm.nih.gov/pubmed/27896977
https://www.proquest.com/docview/1844603562
Volume 22
WOSCitedRecordID wos000391254200022&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Ji9swFBaZoYe2UFq6pcugQm_GbSzbsnVUY01jSGzjSdK0FxPbMgwUT8gszK1_vU_W2NmGwhx6EZGxluiT36b3nhD6LEsnr-TSNStSuqZDSGHmlBUmo04lgSMCeWiy64-9KPIXC5b0en_aWJib315d-7e3bPVfoYZnALYKnX0A3F2n8AB-A-hQAuxQ7knEu7ZXnQmDD8NTWNizn5MkPgtnEyOOjG9hPIwnyWyqLFPAijvWE_CIC6FtcUEYiO7Y6PuIizAK9YnSrw7kkYiCNFw0dIrPw8DgX7a3HDcCIRJjLHgaqaF4kqQxH44M0DSNoboFJ4U3pqLxW2nyWqViLOY8mhrKg86AGURTNf2N3ezOHmF5e_aIH3E6DrSFrGmxo68yX0Vse_7gPpqp2S_RBpBDyu4Q7czR9mHZmRJANnysPbvfY2-d0-G_ejlCRyAHAzmcW6PORAfamgsCjE7TtJn6bt2yd-tkALp_9zLbqljeVoW4bQSXQ74ezuc-fecJetZk0dWRssqRbEsSmj5HT1V0DFZhK_CvX6CerF-iiGOFPG6Rxy3yGJDHGnncIY8BedwijxXyeBf5V2h2KqbDkXl3UYe5cixSma7tljS3_SUtPJB2aOlbzpJKpyCyZKX0PEbU-XsxsEpYH7vwWEXtnEnmVsTPbct-jY7ri1q-Rdi3C-rnBS1zCuyFlEvm5bZXskLdsFbSoo8-tQuTASFUp1vLWl5cX2aW78AYNsjzffRGr1i20hlbMhXuTUHT6aPB3hJm6lOFxk14PskOkegj-tAmWb4-l9W7h4_1Hj3efFEf0PHV-lp-RI-Km6vzy_UJbNGFD2WUTE6ajfoXveqDzA
linkProvider Open Access Publishing in European Networks
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=bookitem&rft.title=PACIFIC+SYMPOSIUM+ON+BIOCOMPUTING+2017&rft.au=DANAEE%2C+PADIDEH&rft.au=GHAEINI%2C+REZA&rft.au=HENDRIX%2C+DAVID+A.&rft.atitle=A+DEEP+LEARNING+APPROACH+FOR+CANCER+DETECTION+AND+RELEVANT+GENE+IDENTIFICATION&rft.date=2017-01-01&rft.pub=WORLD+SCIENTIFIC&rft.isbn=9789813207806&rft.spage=219&rft.epage=229&rft_id=info:doi/10.1142%2F9789813207813_0022&rft.externalDBID=n%2Fa&rft.externalDocID=10.1142%2F9789813207813_0022
thumbnail_s http://cvtisr.summon.serialssolutions.com/2.0.0/image/custom?url=https%3A%2F%2Fwww.worldscientific.com%2Faction%2FshowCoverImage%3Fdoi%3D10.1142%2F9789813207813_0022