Exploring supervised and unsupervised methods to detect topics in biomedical text
Topic detection is a task that automatically identifies topics (e.g., "biochemistry" and "protein structure") in scientific articles based on information content. Topic detection will benefit many other natural language processing tasks including information retrieval, text summa...
Uloženo v:
| Vydáno v: | BMC bioinformatics Ročník 7; číslo 1; s. 140 |
|---|---|
| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
England
BioMed Central
16.03.2006
BMC |
| Témata: | |
| ISSN: | 1471-2105, 1471-2105 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Topic detection is a task that automatically identifies topics (e.g., "biochemistry" and "protein structure") in scientific articles based on information content. Topic detection will benefit many other natural language processing tasks including information retrieval, text summarization and question answering; and is a necessary step towards the building of an information system that provides an efficient way for biologists to seek information from an ocean of literature.
We have explored the methods of Topic Spotting, a task of text categorization that applies the supervised machine-learning technique naïve Bayes to assign automatically a document into one or more predefined topics; and Topic Clustering, which apply unsupervised hierarchical clustering algorithms to aggregate documents into clusters such that each cluster represents a topic. We have applied our methods to detect topics of more than fifteen thousand of articles that represent over sixteen thousand entries in the Online Mendelian Inheritance in Man (OMIM) database. We have explored bag of words as the features. Additionally, we have explored semantic features; namely, the Medical Subject Headings (MeSH) that are assigned to the MEDLINE records, and the Unified Medical Language System (UMLS) semantic types that correspond to the MeSH terms, in addition to bag of words, to facilitate the tasks of topic detection. Our results indicate that incorporating the MeSH terms and the UMLS semantic types as additional features enhances the performance of topic detection and the naïve Bayes has the highest accuracy, 66.4%, for predicting the topic of an OMIM article as one of the total twenty-five topics.
Our results indicate that the supervised topic spotting methods outperformed the unsupervised topic clustering; on the other hand, the unsupervised topic clustering methods have the advantages of being robust and applicable in real world settings. |
|---|---|
| AbstractList | Topic detection is a task that automatically identifies topics (e.g., "biochemistry" and "protein structure") in scientific articles based on information content. Topic detection will benefit many other natural language processing tasks including information retrieval, text summarization and question answering; and is a necessary step towards the building of an information system that provides an efficient way for biologists to seek information from an ocean of literature.BACKGROUNDTopic detection is a task that automatically identifies topics (e.g., "biochemistry" and "protein structure") in scientific articles based on information content. Topic detection will benefit many other natural language processing tasks including information retrieval, text summarization and question answering; and is a necessary step towards the building of an information system that provides an efficient way for biologists to seek information from an ocean of literature.We have explored the methods of Topic Spotting, a task of text categorization that applies the supervised machine-learning technique naïve Bayes to assign automatically a document into one or more predefined topics; and Topic Clustering, which apply unsupervised hierarchical clustering algorithms to aggregate documents into clusters such that each cluster represents a topic. We have applied our methods to detect topics of more than fifteen thousand of articles that represent over sixteen thousand entries in the Online Mendelian Inheritance in Man (OMIM) database. We have explored bag of words as the features. Additionally, we have explored semantic features; namely, the Medical Subject Headings (MeSH) that are assigned to the MEDLINE records, and the Unified Medical Language System (UMLS) semantic types that correspond to the MeSH terms, in addition to bag of words, to facilitate the tasks of topic detection. Our results indicate that incorporating the MeSH terms and the UMLS semantic types as additional features enhances the performance of topic detection and the naïve Bayes has the highest accuracy, 66.4%, for predicting the topic of an OMIM article as one of the total twenty-five topics.RESULTSWe have explored the methods of Topic Spotting, a task of text categorization that applies the supervised machine-learning technique naïve Bayes to assign automatically a document into one or more predefined topics; and Topic Clustering, which apply unsupervised hierarchical clustering algorithms to aggregate documents into clusters such that each cluster represents a topic. We have applied our methods to detect topics of more than fifteen thousand of articles that represent over sixteen thousand entries in the Online Mendelian Inheritance in Man (OMIM) database. We have explored bag of words as the features. Additionally, we have explored semantic features; namely, the Medical Subject Headings (MeSH) that are assigned to the MEDLINE records, and the Unified Medical Language System (UMLS) semantic types that correspond to the MeSH terms, in addition to bag of words, to facilitate the tasks of topic detection. Our results indicate that incorporating the MeSH terms and the UMLS semantic types as additional features enhances the performance of topic detection and the naïve Bayes has the highest accuracy, 66.4%, for predicting the topic of an OMIM article as one of the total twenty-five topics.Our results indicate that the supervised topic spotting methods outperformed the unsupervised topic clustering; on the other hand, the unsupervised topic clustering methods have the advantages of being robust and applicable in real world settings.CONCLUSIONOur results indicate that the supervised topic spotting methods outperformed the unsupervised topic clustering; on the other hand, the unsupervised topic clustering methods have the advantages of being robust and applicable in real world settings. Abstract Background Topic detection is a task that automatically identifies topics (e.g., "biochemistry" and "protein structure") in scientific articles based on information content. Topic detection will benefit many other natural language processing tasks including information retrieval, text summarization and question answering; and is a necessary step towards the building of an information system that provides an efficient way for biologists to seek information from an ocean of literature. Results We have explored the methods of Topic Spotting, a task of text categorization that applies the supervised machine-learning technique naïve Bayes to assign automatically a document into one or more predefined topics; and Topic Clustering, which apply unsupervised hierarchical clustering algorithms to aggregate documents into clusters such that each cluster represents a topic. We have applied our methods to detect topics of more than fifteen thousand of articles that represent over sixteen thousand entries in the Online Mendelian Inheritance in Man (OMIM) database. We have explored bag of words as the features. Additionally, we have explored semantic features; namely, the Medical Subject Headings (MeSH) that are assigned to the MEDLINE records, and the Unified Medical Language System (UMLS) semantic types that correspond to the MeSH terms, in addition to bag of words, to facilitate the tasks of topic detection. Our results indicate that incorporating the MeSH terms and the UMLS semantic types as additional features enhances the performance of topic detection and the naïve Bayes has the highest accuracy, 66.4%, for predicting the topic of an OMIM article as one of the total twenty-five topics. Conclusion Our results indicate that the supervised topic spotting methods outperformed the unsupervised topic clustering; on the other hand, the unsupervised topic clustering methods have the advantages of being robust and applicable in real world settings. Topic detection is a task that automatically identifies topics (e.g., "biochemistry" and "protein structure") in scientific articles based on information content. Topic detection will benefit many other natural language processing tasks including information retrieval, text summarization and question answering; and is a necessary step towards the building of an information system that provides an efficient way for biologists to seek information from an ocean of literature. We have explored the methods of Topic Spotting, a task of text categorization that applies the supervised machine-learning technique naiive Bayes to assign automatically a document into one or more predefined topics; and Topic Clustering, which apply unsupervised hierarchical clustering algorithms to aggregate documents into clusters such that each cluster represents a topic. We have applied our methods to detect topics of more than fifteen thousand of articles that represent over sixteen thousand entries in the Online Mendelian Inheritance in Man (OMIM) database. We have explored bag of words as the features. Additionally, we have explored semantic features; namely, the Medical Subject Headings (MeSH) that are assigned to the MEDLINE records, and the Unified Medical Language System (UMLS) semantic types that correspond to the MeSH terms, in addition to bag of words, to facilitate the tasks of topic detection. Our results indicate that incorporating the MeSH terms and the UMLS semantic types as additional features enhances the performance of topic detection and the naiive Bayes has the highest accuracy, 66.4%, for predicting the topic of an OMIM article as one of the total twenty-five topics. Our results indicate that the supervised topic spotting methods outperformed the unsupervised topic clustering; on the other hand, the unsupervised topic clustering methods have the advantages of being robust and applicable in real world settings. Topic detection is a task that automatically identifies topics (e.g., "biochemistry" and "protein structure") in scientific articles based on information content. Topic detection will benefit many other natural language processing tasks including information retrieval, text summarization and question answering; and is a necessary step towards the building of an information system that provides an efficient way for biologists to seek information from an ocean of literature. We have explored the methods of Topic Spotting, a task of text categorization that applies the supervised machine-learning technique naïve Bayes to assign automatically a document into one or more predefined topics; and Topic Clustering, which apply unsupervised hierarchical clustering algorithms to aggregate documents into clusters such that each cluster represents a topic. We have applied our methods to detect topics of more than fifteen thousand of articles that represent over sixteen thousand entries in the Online Mendelian Inheritance in Man (OMIM) database. We have explored bag of words as the features. Additionally, we have explored semantic features; namely, the Medical Subject Headings (MeSH) that are assigned to the MEDLINE records, and the Unified Medical Language System (UMLS) semantic types that correspond to the MeSH terms, in addition to bag of words, to facilitate the tasks of topic detection. Our results indicate that incorporating the MeSH terms and the UMLS semantic types as additional features enhances the performance of topic detection and the naïve Bayes has the highest accuracy, 66.4%, for predicting the topic of an OMIM article as one of the total twenty-five topics. Our results indicate that the supervised topic spotting methods outperformed the unsupervised topic clustering; on the other hand, the unsupervised topic clustering methods have the advantages of being robust and applicable in real world settings. |
| ArticleNumber | 140 |
| Author | Wang, Weiqing Yu, Hong Lee, Minsuk |
| AuthorAffiliation | 1 Department of Biomedical Informatics, Columbia University, 622West, 168th Street, VC-5, NY 10032, USA 2 Department of Molecular Pharmacology, Albert Einstein College of Medicine, Bronx, NY 10461, USA |
| AuthorAffiliation_xml | – name: 1 Department of Biomedical Informatics, Columbia University, 622West, 168th Street, VC-5, NY 10032, USA – name: 2 Department of Molecular Pharmacology, Albert Einstein College of Medicine, Bronx, NY 10461, USA |
| Author_xml | – sequence: 1 givenname: Minsuk surname: Lee fullname: Lee, Minsuk – sequence: 2 givenname: Weiqing surname: Wang fullname: Wang, Weiqing – sequence: 3 givenname: Hong surname: Yu fullname: Yu, Hong |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/16539745$$D View this record in MEDLINE/PubMed |
| BookMark | eNqFks1r3DAQxUVIyVd77q341JsbydbnpVBC2gYCpZCchSyNNwpeyZXkkPz31WbTZVMoPWkYvffjjTSn6DDEAAi9J_gTIZKfEypI2xHMWtESig_Qya5zuFcfo9Oc7zEmQmJ2hI4JZ70SlJ2gn5eP8xSTD6smLzOkB5_BNSa4Zgl7jTWUu-hyU2LjoIAttZq9zY0PzeDjGpy3ZmoKPJa36M1opgzvXs4zdPv18ubie3v949vVxZfr1lIpS8uGQQEDxzunDFd2GNnIneSUCqXIgLue0dHikYGoMikFFoSMnPKRWeMs68_Q1ZbrornXc_Jrk550NF4_N2JaaZOKtxNox4eRGiOo5D21nKsOV7ZyA8NMyI5W1ucta16GOouFUJKZXkFf3wR_p1fxQdcX7rjqK-DjCyDFXwvkotc-W5gmEyAuWXOJ-5717L9CoigT9DnSh_1Iuyx_vq4K2FZgU8w5waitL6b4uEnoJ02w3qzIJiLRmyXQota4-s7_8u3Q_3D8BsGKvd0 |
| CitedBy_id | crossref_primary_10_1186_1471_2105_14_182 crossref_primary_10_1016_j_jbi_2007_06_004 crossref_primary_10_32604_jai_2024_050706 crossref_primary_10_1227_neu_0000000000002764 crossref_primary_10_1186_1472_6947_12_13 crossref_primary_10_1007_s10586_018_2023_4 crossref_primary_10_1016_j_cmpb_2009_10_003 crossref_primary_10_1371_journal_pone_0108847 crossref_primary_10_1093_bioinformatics_btp338 crossref_primary_10_1016_j_bspc_2022_103539 crossref_primary_10_1016_j_ins_2011_01_029 crossref_primary_10_3758_s13428_021_01764_6 crossref_primary_10_1186_1471_2105_9_406 |
| Cites_doi | 10.1093/bioinformatics/17.2.126 10.1093/nar/gki095 10.1145/345508.345582 10.1101/gr.199701 10.1186/1471-2105-6-S1-S22 10.1093/nar/16.22.10881 10.1093/nar/gki033 10.1186/1471-2105-6-S1-S23 10.3115/1119355.1119372 10.1093/bioinformatics/14.7.600 |
| ContentType | Journal Article |
| Copyright | Copyright © 2006 Lee et al; licensee BioMed Central Ltd. |
| Copyright_xml | – notice: Copyright © 2006 Lee et al; licensee BioMed Central Ltd. |
| DBID | AAYXX CITATION CGR CUY CVF ECM EIF NPM 7QO 8FD FR3 P64 7X8 5PM DOA |
| DOI | 10.1186/1471-2105-7-140 |
| DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed Biotechnology Research Abstracts Technology Research Database Engineering Research Database Biotechnology and BioEngineering Abstracts MEDLINE - Academic PubMed Central (Full Participant titles) Directory of Open Access Journals |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Engineering Research Database Biotechnology Research Abstracts Technology Research Database Biotechnology and BioEngineering Abstracts MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic Engineering Research Database MEDLINE |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Biology |
| EISSN | 1471-2105 |
| EndPage | 140 |
| ExternalDocumentID | oai_doaj_org_article_d6bf4aa748634c669204fc9db5057824 PMC1472693 16539745 10_1186_1471_2105_7_140 |
| Genre | Evaluation Studies Research Support, Non-U.S. Gov't Journal Article |
| GroupedDBID | --- 0R~ 123 23N 2VQ 2WC 4.4 53G 5VS 6J9 AAFWJ AAJSJ AAKPC AASML AAYXX ABDBF ACGFO ACGFS ACIHN ACIWK ACPRK ACUHS ADBBV ADMLS ADRAZ ADUKV AEAQA AENEX AFPKN AFRAH AHBYD AHMBA AHSBF AHYZX ALMA_UNASSIGNED_HOLDINGS AMKLP AMTXH AOIJS BAPOH BAWUL BCNDV BENPR BFQNJ BMC C1A C6C CITATION CS3 DIK DU5 E3Z EAD EAP EAS EBD EBLON EBS EJD EMB EMK EMOBN ESX F5P GROUPED_DOAJ GX1 H13 HYE IAO ICD IHR INH INR IPNFZ ISR ITC KQ8 M48 MK~ ML0 M~E O5R O5S OK1 OVT P2P PGMZT PIMPY PQQKQ RBZ RIG RNS ROL RPM RSV SBL SOJ SV3 TR2 TUS W2D WOQ WOW XH6 XSB -A0 ACRMQ ADINQ ALIPV C24 CGR CUY CVF ECM EIF NPM 7QO 8FD FR3 P64 7X8 5PM |
| ID | FETCH-LOGICAL-c488t-5bb9e5ed62d9a69cbf5f6d86447991b02354fc0f5e7e5e8870711f646f5cadc53 |
| IEDL.DBID | DOA |
| ISICitedReferencesCount | 23 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000237981800001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1471-2105 |
| IngestDate | Fri Oct 03 12:39:41 EDT 2025 Thu Aug 21 17:59:16 EDT 2025 Fri Sep 05 09:14:15 EDT 2025 Tue Oct 07 09:18:27 EDT 2025 Wed Feb 19 01:47:19 EST 2025 Sat Nov 29 02:17:57 EST 2025 Tue Nov 18 20:25:48 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Language | English |
| License | This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c488t-5bb9e5ed62d9a69cbf5f6d86447991b02354fc0f5e7e5e8870711f646f5cadc53 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 ObjectType-Article-2 ObjectType-Undefined-1 ObjectType-Feature-3 |
| OpenAccessLink | https://doaj.org/article/d6bf4aa748634c669204fc9db5057824 |
| PMID | 16539745 |
| PQID | 19457424 |
| PQPubID | 23462 |
| PageCount | 1 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_d6bf4aa748634c669204fc9db5057824 pubmedcentral_primary_oai_pubmedcentral_nih_gov_1472693 proquest_miscellaneous_68033535 proquest_miscellaneous_19457424 pubmed_primary_16539745 crossref_citationtrail_10_1186_1471_2105_7_140 crossref_primary_10_1186_1471_2105_7_140 |
| PublicationCentury | 2000 |
| PublicationDate | 2006-03-16 |
| PublicationDateYYYYMMDD | 2006-03-16 |
| PublicationDate_xml | – month: 03 year: 2006 text: 2006-03-16 day: 16 |
| PublicationDecade | 2000 |
| PublicationPlace | England |
| PublicationPlace_xml | – name: England – name: London |
| PublicationTitle | BMC bioinformatics |
| PublicationTitleAlternate | BMC Bioinformatics |
| PublicationYear | 2006 |
| Publisher | BioMed Central BMC |
| Publisher_xml | – name: BioMed Central – name: BMC |
| References | T Joachims (879_CR3) 1998 879_CR5 LJ Smink (879_CR1) 2005; 33 WJ Wilbur (879_CR4) 2002 S Rice (879_CR7) 2005; 6 J Herrero (879_CR15) 2001; 17 F Ehrler (879_CR8) 2005; 6 Suppl 1 I Witten (879_CR16) 1999 S Raychaudhuri (879_CR6) 2002; 1 A Hamosh (879_CR9) 2005; 33 NIST (879_CR10) 1998 V Hatzivassiloglou (879_CR11) 2000 F Corpet (879_CR14) 1988; 16 H Yu (879_CR12) 1999 H Yu (879_CR2) 2003 MA Andrade (879_CR13) 1998; 14 10566345 - Proc AMIA Symp. 1999;:181-5 9730925 - Bioinformatics. 1998;14(7):600-7 11779846 - Genome Res. 2002 Jan;12(1):203-14 15960835 - BMC Bioinformatics. 2005;6 Suppl 1:S22 11238068 - Bioinformatics. 2001 Feb;17(2):126-36 2849754 - Nucleic Acids Res. 1988 Nov 25;16(22):10881-90 11928492 - Pac Symp Biocomput. 2002;:386-97 15960836 - BMC Bioinformatics. 2005;6 Suppl 1:S23 15608251 - Nucleic Acids Res. 2005 Jan 1;33(Database issue):D514-7 15608258 - Nucleic Acids Res. 2005 Jan 1;33(Database issue):D544-9 |
| References_xml | – start-page: 386 volume-title: Pac Symp Biocomput year: 2002 ident: 879_CR4 – volume: 17 start-page: 126 issue: 2 year: 2001 ident: 879_CR15 publication-title: Bioinformatics doi: 10.1093/bioinformatics/17.2.126 – volume: 33 start-page: D544 issue: Database issue year: 2005 ident: 879_CR1 publication-title: Nucleic Acids Res doi: 10.1093/nar/gki095 – volume-title: An investigation of linguistic features and clustering algorithms for topical document clustering. year: 2000 ident: 879_CR11 doi: 10.1145/345508.345582 – volume: 1 start-page: 203 year: 2002 ident: 879_CR6 publication-title: Genome Research doi: 10.1101/gr.199701 – volume: 6 start-page: S22 issue: Suppl 1 year: 2005 ident: 879_CR7 publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-6-S1-S22 – volume: 16 start-page: 10881 issue: 22 year: 1988 ident: 879_CR14 publication-title: Nucleic Acids Res doi: 10.1093/nar/16.22.10881 – volume: 33 start-page: D514 issue: Database issue year: 2005 ident: 879_CR9 publication-title: Nucleic Acids Res doi: 10.1093/nar/gki033 – volume-title: Managing Gigabytes: Compressing and indexing documents and images. We have used Managing Gigabytes for JavaTM. Available at http://mg4j.dsi.unimi.it. year: 1999 ident: 879_CR16 – start-page: 181 volume-title: Proc AMIA Symp year: 1999 ident: 879_CR12 – ident: 879_CR5 – start-page: 137 volume-title: Text categorization with support vector machines: Learning with many relevant features year: 1998 ident: 879_CR3 – volume: 6 Suppl 1 start-page: S23 year: 2005 ident: 879_CR8 publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-6-S1-S23 – volume-title: Towards answering opinion questions: Separating facts from opinions and identifying the polarity of opinion sentences. year: 2003 ident: 879_CR2 doi: 10.3115/1119355.1119372 – volume: 14 start-page: 600 issue: 7 year: 1998 ident: 879_CR13 publication-title: Bioinformatics doi: 10.1093/bioinformatics/14.7.600 – volume-title: The topic detection and tracking phase 2 (TDT2) evaluation plan Version 3.7. year: 1998 ident: 879_CR10 – reference: 15960836 - BMC Bioinformatics. 2005;6 Suppl 1:S23 – reference: 10566345 - Proc AMIA Symp. 1999;:181-5 – reference: 2849754 - Nucleic Acids Res. 1988 Nov 25;16(22):10881-90 – reference: 9730925 - Bioinformatics. 1998;14(7):600-7 – reference: 15960835 - BMC Bioinformatics. 2005;6 Suppl 1:S22 – reference: 15608258 - Nucleic Acids Res. 2005 Jan 1;33(Database issue):D544-9 – reference: 11238068 - Bioinformatics. 2001 Feb;17(2):126-36 – reference: 15608251 - Nucleic Acids Res. 2005 Jan 1;33(Database issue):D514-7 – reference: 11779846 - Genome Res. 2002 Jan;12(1):203-14 – reference: 11928492 - Pac Symp Biocomput. 2002;:386-97 |
| SSID | ssj0017805 |
| Score | 1.9664998 |
| Snippet | Topic detection is a task that automatically identifies topics (e.g., "biochemistry" and "protein structure") in scientific articles based on information... Abstract Background Topic detection is a task that automatically identifies topics (e.g., "biochemistry" and "protein structure") in scientific articles based... |
| SourceID | doaj pubmedcentral proquest pubmed crossref |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source |
| StartPage | 140 |
| SubjectTerms | Abstracting and Indexing as Topic - methods Artificial Intelligence Humans MEDLINE Natural Language Processing Pattern Recognition, Automated - methods Periodicals as Topic Terminology as Topic Vocabulary, Controlled |
| Title | Exploring supervised and unsupervised methods to detect topics in biomedical text |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/16539745 https://www.proquest.com/docview/19457424 https://www.proquest.com/docview/68033535 https://pubmed.ncbi.nlm.nih.gov/PMC1472693 https://doaj.org/article/d6bf4aa748634c669204fc9db5057824 |
| Volume | 7 |
| WOSCitedRecordID | wos000237981800001&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 | |
| journalDatabaseRights | – providerCode: PRVADU databaseName: BioMed Central_OA刊 customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: RBZ dateStart: 20000101 isFulltext: true titleUrlDefault: https://www.biomedcentral.com/search/ providerName: BioMedCentral – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: DOA dateStart: 20000101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: M~E dateStart: 20000101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVAVX databaseName: SpringerLINK Contemporary 1997-Present customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: RSV dateStart: 20001201 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LixQxEA66KHgR346PNQcPXuJ2d5JKclTZxYuLgsLcQp44ID3D9ozgZX_7Vjo944z4uHhpQlINSVUlqUoqXxHyUkjHXZSKpQTooARtmGu1Zz5EpSB3EJMYk02o83M9n5uPe6m-SkxYhQeujDuJ4LNwTgkNXAQA0zUiBxN9sax1NyKBNspsnanp_qAg9Y_vilTL0KmRE6hPq-FkV8cUa8uZx95-NML2_87W_DVkcm8POrtDbk_GI31TO32XXEv9PXKzppP8cZ982gXU0WGzKovAkCJ1faSbfq-iJo0e6HpJYyp3CFhaLcJAFz2tr_GL4GgJCXlAvpydfn73nk0pE1jAmbhm0nuTZIrQRePABJ9lhqjR6FFoCPoCboPca7JMCslwgUELo80gIMvgYpD8ITnql316TCiP6InlhP4Y5wJih5ZlLC8WtJYpax9m5PWWcTZMeOIlrcU3O_oVGmzhtC2ctgrLzYy82v2wqlAafyZ9WySxIysY2GMFaoadNMP-SzNm5MVWjhbnTLkIcX1abgbbGiGV-BsF6IZzyeWMPKpy_9njguWrBLaoA4046OthS7_4OuJ24yA7MPzJ_xjcU3KrHgZx1sIzcrS-2KTn5Eb4vl4MF8fkuprr43FK4PfD5ekV1-oOug |
| linkProvider | Directory of Open Access Journals |
| 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=Exploring+supervised+and+unsupervised+methods+to+detect+topics+in+biomedical+text&rft.jtitle=BMC+bioinformatics&rft.au=Lee%2C+Minsuk&rft.au=Wang%2C+Weiqing&rft.au=Yu%2C+Hong&rft.date=2006-03-16&rft.pub=BioMed+Central&rft.eissn=1471-2105&rft.volume=7&rft.spage=140&rft.epage=140&rft_id=info:doi/10.1186%2F1471-2105-7-140&rft_id=info%3Apmid%2F16539745&rft.externalDocID=PMC1472693 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1471-2105&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1471-2105&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1471-2105&client=summon |