Binary tree-structured vector quantization approach to clustering and visualizing microarray data
Motivation: With the increasing number of gene expression databases, the need for more powerful analysis and visualization tools is growing. Many techniques have successfully been applied to unravel latent similarities among genes and/or experiments. Most of the current systems for microarray data a...
Uložené v:
| Vydané v: | Bioinformatics Ročník 18; číslo suppl_1; s. S111 - S119 |
|---|---|
| Hlavní autori: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
England
Oxford Publishing Limited (England)
01.07.2002
|
| Predmet: | |
| ISSN: | 1367-4803, 1367-4811, 1460-2059 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | Motivation: With the increasing number of gene expression databases, the need for more powerful analysis and visualization tools is growing. Many techniques have successfully been applied to unravel latent similarities among genes and/or experiments. Most of the current systems for microarray data analysis use statistical methods, hierarchical clustering, self-organizing maps, support vector machines, or k-means clustering to organize genes or experiments into ‘meaningful’ groups. Without prior explicit bias almost all of these clustering methods applied to gene expression data not only produce different results, but may also produce clusters with little or no biological relevance. Of these methods, agglomerative hierarchical clustering has been the most widely applied, although many limitations have been identified.
Results: Starting with a systematic comparison of the underlying theories behind clustering approaches, we have devised a technique that combines tree-structured vector quantization and partitive k-means clustering (BTSVQ). This hybrid technique has revealed clinically relevant clusters in three large publicly available data sets. In contrast to existing systems, our approach is less sensitive to data preprocessing and data normalization. In addition, the clustering results produced by the technique have strong similarities to those of self-organizing maps (SOMs). We discuss the advantages and the mathematical reasoning behind our approach.
Availability: The BTSVQ system is implemented in Matlab R12 using the SOM toolbox for the visualization and preprocessing of the data http://www.cis.hut.fi/projects/somtoolbox/ BTSVQ is available for non-commercial use http://www.uhnres.utoronto.ca/ta3/BTSVQ
Contact: ij@uhnres.utoronto.ca
Keywords: microarray data clustering and visulization; self-organizing maps, partitive k-means clustering; lung cancer. |
|---|---|
| AbstractList | Motivation: With the increasing number of gene expression databases, the need for more powerful analysis and visualization tools is growing. Many techniques have successfully been applied to unravel latent similarities among genes and/or experiments. Most of the current systems for microarray data analysis use statistical methods, hierarchical clustering, self-organizing maps, support vector machines, or k-means clustering to organize genes or experiments into 'meaningful' groups. Without prior explicit bias almost all of these clustering methods applied to gene expression data not only produce different results, but may also produce clusters with little or no biological relevance. Of these methods, agglomerative hierarchical clustering has been the most widely applied, although many limitations have been identified. Results: Starting with a systematic comparison of the underlying theories behind clustering approaches, we have devised a technique that combines tree-structured vector quantization and partitive k-means clustering (BTSVQ). This hybrid technique has revealed clinically relevant clusters in three large publicly available data sets. In contrast to existing systems, our approach is less sensitive to data preprocessing and data normalization. In addition, the clustering results produced by the technique have strong similarities to those of self-organizing maps (SOMs). We discuss the advantages and the mathematical reasoning behind our approach. Availability: The BTSVQ system is implemented in Matlab R12 using the SOM toolbox for the visualization and preprocessing of the data http://www.cis.hut.fi/projects/somtoolbox/ BTSVQ is available for non-commercial use http://www.uhnres.utoronto.ca/ta3/BTSVQ Contact: ij[at]uhnres.utoronto.ca Keywords: microarray data clustering and visulization; self-organizing maps, partitive k-means clustering; lung cancer. Motivation: With the increasing number of gene expression databases, the need for more powerful analysis and visualization tools is growing. Many techniques have successfully been applied to unravel latent similarities among genes and/or experiments. Most of the current systems for microarray data analysis use statistical methods, hierarchical clustering, self-organizing maps, support vector machines, or k-means clustering to organize genes or experiments into ‘meaningful’ groups. Without prior explicit bias almost all of these clustering methods applied to gene expression data not only produce different results, but may also produce clusters with little or no biological relevance. Of these methods, agglomerative hierarchical clustering has been the most widely applied, although many limitations have been identified. Results: Starting with a systematic comparison of the underlying theories behind clustering approaches, we have devised a technique that combines tree-structured vector quantization and partitive k-means clustering (BTSVQ). This hybrid technique has revealed clinically relevant clusters in three large publicly available data sets. In contrast to existing systems, our approach is less sensitive to data preprocessing and data normalization. In addition, the clustering results produced by the technique have strong similarities to those of self-organizing maps (SOMs). We discuss the advantages and the mathematical reasoning behind our approach. Availability: The BTSVQ system is implemented in Matlab R12 using the SOM toolbox for the visualization and preprocessing of the data http://www.cis.hut.fi/projects/somtoolbox/ BTSVQ is available for non-commercial use http://www.uhnres.utoronto.ca/ta3/BTSVQ Contact: ij@uhnres.utoronto.ca Keywords: microarray data clustering and visulization; self-organizing maps, partitive k-means clustering; lung cancer. Motivation: With the increasing number of gene expression databases, the need for more powerful analysis and visualization tools is growing. Many techniques have successfully been applied to unravel latent similarities among genes and/or experiments. Most of the current systems for microarray data analysis use statistical methods, hierarchical clustering, self-organizing maps, support vector machines, or k-means clustering to organize genes or experiments into 'meaningful' groups. Without prior explicit bias almost all of these clustering methods applied to gene expression data not only produce different results, but may also produce clusters with little or no biological relevance. Of these methods, agglomerative hierarchical clustering has been the most widely applied, although many limitations have been identified. Results: Starting with a systematic comparison of the underlying theories behind clustering approaches, we have devised a technique that combines tree-structured vector quantization and partitive k-means clustering (BTSVQ). This hybrid technique has revealed clinically relevant clusters in three large publicly available data sets. In contrast to existing systems, our approach is less sensitive to data preprocessing and data normalization. In addition, the clustering results produced by the technique have strong similarities to those of self-organizing maps (SOMs). We discuss the advantages and the mathematical reasoning behind our approach. Availability: The BTSVQ system is implemented in Matlab R12 using the SOM toolbox for the visualization and preprocessing of the data http://www.cis.hut.fi/projects/somtoolbox/ BTSVQ is available for non-commercial use http://www.uhnres.utoronto.ca/ta3/BTSVQ Contact: ij@uhnres.utoronto.ca Keywords: microarray data clustering and visulization; self-organizing maps, partitive k-means clustering; lung cancer. With the increasing number of gene expression databases, the need for more powerful analysis and visualization tools is growing. Many techniques have successfully been applied to unravel latent similarities among genes and/or experiments. Most of the current systems for microarray data analysis use statistical methods, hierarchical clustering, self-organizing maps, support vector machines, or k-means clustering to organize genes or experiments into 'meaningful' groups. Without prior explicit bias almost all of these clustering methods applied to gene expression data not only produce different results, but may also produce clusters with little or no biological relevance. Of these methods, agglomerative hierarchical clustering has been the most widely applied, although many limitations have been identified.MOTIVATIONWith the increasing number of gene expression databases, the need for more powerful analysis and visualization tools is growing. Many techniques have successfully been applied to unravel latent similarities among genes and/or experiments. Most of the current systems for microarray data analysis use statistical methods, hierarchical clustering, self-organizing maps, support vector machines, or k-means clustering to organize genes or experiments into 'meaningful' groups. Without prior explicit bias almost all of these clustering methods applied to gene expression data not only produce different results, but may also produce clusters with little or no biological relevance. Of these methods, agglomerative hierarchical clustering has been the most widely applied, although many limitations have been identified.Starting with a systematic comparison of the underlying theories behind clustering approaches, we have devised a technique that combines tree-structured vector quantization and partitive k-means clustering (BTSVQ). This hybrid technique has revealed clinically relevant clusters in three large publicly available data sets. In contrast to existing systems, our approach is less sensitive to data preprocessing and data normalization. In addition, the clustering results produced by the technique have strong similarities to those of self-organizing maps (SOMs). We discuss the advantages and the mathematical reasoning behind our approach.RESULTSStarting with a systematic comparison of the underlying theories behind clustering approaches, we have devised a technique that combines tree-structured vector quantization and partitive k-means clustering (BTSVQ). This hybrid technique has revealed clinically relevant clusters in three large publicly available data sets. In contrast to existing systems, our approach is less sensitive to data preprocessing and data normalization. In addition, the clustering results produced by the technique have strong similarities to those of self-organizing maps (SOMs). We discuss the advantages and the mathematical reasoning behind our approach. With the increasing number of gene expression databases, the need for more powerful analysis and visualization tools is growing. Many techniques have successfully been applied to unravel latent similarities among genes and/or experiments. Most of the current systems for microarray data analysis use statistical methods, hierarchical clustering, self-organizing maps, support vector machines, or k-means clustering to organize genes or experiments into 'meaningful' groups. Without prior explicit bias almost all of these clustering methods applied to gene expression data not only produce different results, but may also produce clusters with little or no biological relevance. Of these methods, agglomerative hierarchical clustering has been the most widely applied, although many limitations have been identified. Starting with a systematic comparison of the underlying theories behind clustering approaches, we have devised a technique that combines tree-structured vector quantization and partitive k-means clustering (BTSVQ). This hybrid technique has revealed clinically relevant clusters in three large publicly available data sets. In contrast to existing systems, our approach is less sensitive to data preprocessing and data normalization. In addition, the clustering results produced by the technique have strong similarities to those of self-organizing maps (SOMs). We discuss the advantages and the mathematical reasoning behind our approach. With the increasing number of gene expression databases, the need for more powerful analysis and visualization tools is growing. Many techniques have successfully been applied to unravel latent similarities among genes and/or experiments. Most of the current systems for microarray data analysis use statistical methods, hierarchical clustering, self-organizing maps, support vector machines, or k-means clustering to organize genes or experiments into 'meaningful' groups. Without prior explicit bias almost all of these clustering methods applied to gene expression data not only produce different results, but may also produce clusters with little or no biological relevance. Of these methods, agglomerative hierarchical clustering has been the most widely applied, although many limitations have been identified. |
| Author | Cumbaa, C.A. Wigle, D.A. Tsao, M.S. Jurisica, I. Glasgow, J. Sultan, M. Maziarz, M. |
| Author_xml | – sequence: 1 givenname: M. surname: Sultan fullname: Sultan, M. – sequence: 2 givenname: D.A. surname: Wigle fullname: Wigle, D.A. – sequence: 3 givenname: C.A. surname: Cumbaa fullname: Cumbaa, C.A. – sequence: 4 givenname: M. surname: Maziarz fullname: Maziarz, M. – sequence: 5 givenname: J. surname: Glasgow fullname: Glasgow, J. – sequence: 6 givenname: M.S. surname: Tsao fullname: Tsao, M.S. – sequence: 7 givenname: I. surname: Jurisica fullname: Jurisica, I. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/12169538$$D View this record in MEDLINE/PubMed |
| BookMark | eNqNkctO3TAURa2KqjzaX0ARA2a5-BUnniBRWgoSUgfQsXXiOGCU2MGPSvD19YXbIpjQkX2kpb3ts3bRlvPOIHRI8IpgyY56660bfZghWR2PSLeKeVkmRVZXhJAPaIcw0da8I2Tr3x2zbbQb4x3GuMGN-IS2CSVCNqzbQfDVOggPVQrG1DGFrFMOZqh-G518qO4zuGQfS5l3FSxL8KBvq-QrPeWYTLDupgJXcBszTPZxPc9WFywEeKgGSPAZfRxhiubL5txDv86-X5-e15c_f1ycnlzWmtE21RxaPAIxgpG2F63UAnTPZYM5xVq0lPUjLaOQgwQ6Utn0nA6SadNjxgeO2R46fM4tj7zPJiY126jNNIEzPkfVEtlJLsS7IMVcMkbluyDpREdbwgt48Aa88zm48ltVSgVvBFvX7m-g3M9mUEuwc9m8-uuiAMfPQFlfjMGMLwhWa_nqtfzSrzby1Vp-Cfj2JkDb9KQuBbDT_8b8AWRqw4s |
| CODEN | BOINFP |
| CitedBy_id | crossref_primary_10_1136_ijgc_2018_000087 crossref_primary_10_1002_biot_201700503 crossref_primary_10_1016_j_neunet_2011_06_012 crossref_primary_10_1093_genetics_165_3_997 crossref_primary_10_1007_s10898_004_7390_0 crossref_primary_10_1002_ijc_20197 crossref_primary_10_1186_1471_2105_15_S6_I1 crossref_primary_10_1007_s10898_004_2706_7 crossref_primary_10_1158_1078_0432_CCR_07_4959 crossref_primary_10_1109_TSMCC_2006_879384 crossref_primary_10_1016_j_compbiolchem_2003_09_006 crossref_primary_10_1002_cncr_21293 crossref_primary_10_1038_sj_onc_1209005 crossref_primary_10_1089_cmb_2008_0161 crossref_primary_10_1002_aris_2009_1440430108 crossref_primary_10_1016_j_lungcan_2004_04_002 crossref_primary_10_1593_neo_04301 crossref_primary_10_1016_j_placenta_2011_05_005 crossref_primary_10_1109_TCBB_2005_55 crossref_primary_10_1186_1471_2105_14_191 crossref_primary_10_1016_j_berh_2024_102006 crossref_primary_10_1109_TKDE_2007_190649 crossref_primary_10_1109_TNN_2006_882370 crossref_primary_10_1111_j_1742_4658_2006_05176_x crossref_primary_10_1126_science_1105776 crossref_primary_10_1210_jc_2005_0078 crossref_primary_10_2196_jmir_7412 crossref_primary_10_1016_j_placenta_2004_01_025 crossref_primary_10_1200_JCO_2007_12_0352 crossref_primary_10_1038_sj_onc_1207795 crossref_primary_10_15252_msb_20145136 crossref_primary_10_1002_ijc_22107 crossref_primary_10_1038_sj_onc_1209773 |
| ContentType | Journal Article |
| Copyright | Copyright Oxford University Press(England) Jul 2002 |
| Copyright_xml | – notice: Copyright Oxford University Press(England) Jul 2002 |
| DBID | AAYXX CITATION CGR CUY CVF ECM EIF NPM 7QF 7QO 7QQ 7SC 7SE 7SP 7SR 7TA 7TB 7TM 7TO 7U5 8BQ 8FD F28 FR3 H8D H8G H94 JG9 JQ2 K9. KR7 L7M L~C L~D P64 7X8 |
| DOI | 10.1093/bioinformatics/18.suppl_1.S111 |
| DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed Aluminium Industry Abstracts Biotechnology Research Abstracts Ceramic Abstracts Computer and Information Systems Abstracts Corrosion Abstracts Electronics & Communications Abstracts Engineered Materials Abstracts Materials Business File Mechanical & Transportation Engineering Abstracts Nucleic Acids Abstracts Oncogenes and Growth Factors Abstracts Solid State and Superconductivity Abstracts METADEX Technology Research Database ANTE: Abstracts in New Technology & Engineering Engineering Research Database Aerospace Database Copper Technical Reference Library AIDS and Cancer Research Abstracts Materials Research Database ProQuest Computer Science Collection ProQuest Health & Medical Complete (Alumni) Civil Engineering Abstracts Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Biotechnology and BioEngineering Abstracts MEDLINE - Academic |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Materials Research Database Oncogenes and Growth Factors Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Mechanical & Transportation Engineering Abstracts Nucleic Acids Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts ProQuest Health & Medical Complete (Alumni) Materials Business File Aerospace Database Copper Technical Reference Library Engineered Materials Abstracts Biotechnology Research Abstracts AIDS and Cancer Research Abstracts Advanced Technologies Database with Aerospace ANTE: Abstracts in New Technology & Engineering Civil Engineering Abstracts Aluminium Industry Abstracts Electronics & Communications Abstracts Ceramic Abstracts METADEX Biotechnology and BioEngineering Abstracts Computer and Information Systems Abstracts Professional Solid State and Superconductivity Abstracts Engineering Research Database Corrosion Abstracts MEDLINE - Academic |
| DatabaseTitleList | Engineering Research Database CrossRef Materials Research Database MEDLINE - Academic MEDLINE Engineering Research Database |
| 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 |
| Discipline | Biology |
| EISSN | 1367-4811 1460-2059 |
| EndPage | S119 |
| ExternalDocumentID | 431263011 12169538 10_1093_bioinformatics_18_suppl_1_S111 |
| Genre | Validation Studies Evaluation Studies Research Support, Non-U.S. Gov't Journal Article |
| GroupedDBID | --- -E4 -~X .-4 .2P .DC .GJ .I3 0R~ 1TH 23N 2WC 4.4 48X 53G 5GY 5WA 70D AAIJN AAIMJ AAJKP AAKPC AAMDB AAMVS AAOGV AAPQZ AAPXW AAUQX AAVAP AAVLN AAYXX ABEFU ABEJV ABEUO ABGNP ABIXL ABNGD ABNKS ABPQP ABPTD ABQLI ABWST ABXVV ABZBJ ACGFS ACIWK ACPRK ACUFI ACUKT ACUXJ ACYTK ADBBV ADEYI ADEZT ADFTL ADGKP ADGZP ADHKW ADHZD ADMLS ADOCK ADPDF ADRDM ADRTK ADVEK ADYVW ADZTZ ADZXQ AECKG AEGPL AEJOX AEKKA AEKSI AELWJ AEMDU AENEX AENZO AEPUE AETBJ AEWNT AFFNX AFFZL AFGWE AFIYH AFOFC AFRAH AGINJ AGKEF AGQPQ AGQXC AGSYK AHMBA AHXPO AI. AIJHB AJEEA AJEUX AKHUL AKWXX ALMA_UNASSIGNED_HOLDINGS ALTZX ALUQC AMNDL APIBT APWMN ARIXL ASPBG AVWKF AXUDD AYOIW AZFZN AZVOD BAWUL BAYMD BHONS BQDIO BQUQU BSWAC BTQHN C1A C45 CAG CDBKE CITATION COF CS3 CZ4 DAKXR DIK DILTD DU5 D~K EBD EBS EE~ EJD EMOBN F5P F9B FEDTE FHSFR FLIZI FLUFQ FOEOM FQBLK GAUVT GJXCC GROUPED_DOAJ GX1 H5~ HAR HVGLF HW0 HZ~ IOX J21 JXSIZ KAQDR KOP KQ8 KSI KSN M-Z MK~ ML0 N9A NGC NLBLG NMDNZ NOMLY NTWIH NVLIB O0~ O9- OAWHX ODMLO OJQWA OK1 OVD OVEED P2P PAFKI PB- PEELM PQQKQ Q1. Q5Y R44 RD5 RNI RNS ROL ROX RUSNO RW1 RXO RZF RZO SV3 TEORI TJP TLC TOX TR2 VH1 W8F WOQ X7H YAYTL YKOAZ YXANX ZGI ZKX ~91 ~KM AAJQQ ABQTQ ADRIX AFXEN AQDSO ATTQO BCRHZ CGR CUY CVF ECM EIF ELUNK H13 M49 NPM NU- O~Y RIG RPM XJT 7QF 7QO 7QQ 7SC 7SE 7SP 7SR 7TA 7TB 7TM 7TO 7U5 8BQ 8FD F28 FR3 H8D H8G H94 JG9 JQ2 K9. KR7 L7M L~C L~D P64 482 ABJNI ROZ TN5 WH7 7X8 |
| ID | FETCH-LOGICAL-c327t-4a70fa1e6317b679c6acb4950420c6723bf249569d9a2f295b42d93ceb034d403 |
| ISSN | 1367-4803 |
| IngestDate | Sun Nov 09 11:23:55 EST 2025 Tue Oct 07 09:38:11 EDT 2025 Mon Oct 06 18:05:46 EDT 2025 Fri Oct 03 09:51:18 EDT 2025 Wed Feb 19 02:35:37 EST 2025 Tue Nov 18 22:23:23 EST 2025 Sat Nov 29 05:33:47 EST 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | suppl_1 |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c327t-4a70fa1e6317b679c6acb4950420c6723bf249569d9a2f295b42d93ceb034d403 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 content type line 14 ObjectType-Article-2 ObjectType-Feature-1 content type line 23 ObjectType-Feature-2 ObjectType-Undefined-1 ObjectType-Feature-3 |
| PMID | 12169538 |
| PQID | 198645636 |
| PQPubID | 23462 |
| PageCount | 1 |
| ParticipantIDs | proquest_miscellaneous_71989466 proquest_miscellaneous_20493329 proquest_miscellaneous_18682714 proquest_journals_198645636 pubmed_primary_12169538 crossref_primary_10_1093_bioinformatics_18_suppl_1_S111 crossref_citationtrail_10_1093_bioinformatics_18_suppl_1_S111 |
| PublicationCentury | 2000 |
| PublicationDate | 20020701 |
| PublicationDateYYYYMMDD | 2002-07-01 |
| PublicationDate_xml | – month: 07 year: 2002 text: 20020701 day: 01 |
| PublicationDecade | 2000 |
| PublicationPlace | England |
| PublicationPlace_xml | – name: England – name: Oxford |
| PublicationTitle | Bioinformatics |
| PublicationTitleAlternate | Bioinformatics |
| PublicationYear | 2002 |
| Publisher | Oxford Publishing Limited (England) |
| Publisher_xml | – name: Oxford Publishing Limited (England) |
| SSID | ssj0005056 ssj0051444 |
| Score | 1.963107 |
| Snippet | Motivation: With the increasing number of gene expression databases, the need for more powerful analysis and visualization tools is growing. Many techniques... With the increasing number of gene expression databases, the need for more powerful analysis and visualization tools is growing. Many techniques have... |
| SourceID | proquest pubmed crossref |
| SourceType | Aggregation Database Index Database Enrichment Source |
| StartPage | S111 |
| SubjectTerms | Algorithms Carcinoma, Non-Small-Cell Lung - genetics Cluster Analysis Computer Graphics Gene Expression Profiling - methods Humans Lung cancer Lung Neoplasms - genetics Models, Genetic Models, Statistical Oligonucleotide Array Sequence Analysis - methods Software Statistical methods User-Computer Interface |
| Title | Binary tree-structured vector quantization approach to clustering and visualizing microarray data |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/12169538 https://www.proquest.com/docview/198645636 https://www.proquest.com/docview/18682714 https://www.proquest.com/docview/20493329 https://www.proquest.com/docview/71989466 |
| Volume | 18 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVASL databaseName: Oxford Journals Open Access Collection customDbUrl: eissn: 1367-4811 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0005056 issn: 1367-4803 databaseCode: TOX dateStart: 19850101 isFulltext: true titleUrlDefault: https://academic.oup.com/journals/ providerName: Oxford University Press – providerCode: PRVASL databaseName: Oxford Journals Open Access Collection customDbUrl: eissn: 1367-4811 dateEnd: 20220930 omitProxy: false ssIdentifier: ssj0005056 issn: 1367-4803 databaseCode: TOX dateStart: 19850101 isFulltext: true titleUrlDefault: https://academic.oup.com/journals/ providerName: Oxford University Press |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1LbxMxELbS8hAXxJtQKD6gXtDSXXtjrw8coLTiUFKkplJuK-8jVaR0E9JNlObn8kuYWXtfqYLKgcsq67U3E8-X8Xg8D0I-eJ6KA6FSJ0iEKI4ZHS3R11XJuOcmAR8lSVFsQvb7wXCofnY6v8tYmOVEZlmwWqnZf2U1tAGzMXT2H9hdvRQa4DMwHa7AdrjeifFfTYQtnjY7JjvsAn3Ml4V5HoMos9zGXlYJxVEBjScLzJlQxiwux9cYbrnG-yt02tPzub75aEPZ6nPg8dSmXi3SPWPu0lXpLm_rgzRsDeeLSW4Mrg17z6VxaP5WW1WPFleRLpTahqn1h16P9XxdDy5NFbVba94MgWzY12wQF1K3QZORxhyTsgeukYBps81K6FsivIKqkcdWkt9aJ0wOrag1R2jDCGDJAu0_9D6dl4tAK0V3_yw8uTg9DQfHw0H7aaESgDbGBIrMg9kvByuboQeALfOyQ-4x2VPodjg4G9ZuSG5RYbj6qQ_JgSXwsE3e4QZxbUVqy-6o0JIGT8hju72hXwwsn5JOmj0jD0zB05vnRBtw0g1wUgNO2gQnLcFJ8ymtwUmBe7QBTlqDkyI4X5CLk-PB0XfHFvlwYs5k7vhauiPtpQIU2UhIFQsdR7Brh8XEjYVkPBpheXShEqXZiKle5LNE8TiNXO4nvstfkt1smqWvCQ2Um_pepIQvIl-MlHZBmcY72AKB5u11yedyxsLYZsDHQiwwo4UnBg_bMx56QWhnPMQZ7xJZjZ-ZXDB3HrlXMii0ogL6YGWEnuCiS95XT0G444mdztLpAl8jAiY9f3sPBjt8zpna3kOiV6Qv4FteGWTUtDNPKFB43vyVuj3yqP4vvyW7gI30HbkfL_Px9Xyf7MhhsF9A-g-HO-tx |
| linkProvider | Oxford University Press |
| 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%3Ajournal&rft.genre=article&rft.atitle=Binary+tree-structured+vector+quantization+approach+to+clustering+and+visualizing+microarray+data&rft.jtitle=Bioinformatics+%28Oxford%2C+England%29&rft.au=Sultan%2C+M&rft.au=Wigle%2C+D+A&rft.au=Cumbaa%2C+C+A&rft.au=Maziarz%2C+M&rft.date=2002-07-01&rft.pub=Oxford+Publishing+Limited+%28England%29&rft.issn=1367-4803&rft.eissn=1367-4811&rft.volume=18&rft.issue=1&rft.spage=111&rft_id=info:doi/10.1093%2Fbioinformatics%2F18.suppl_1.S111&rft.externalDBID=NO_FULL_TEXT&rft.externalDocID=431263011 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1367-4803&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1367-4803&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1367-4803&client=summon |