Semi-automated literature mining to identify putative biomarkers of disease from multiple biofluids
Identifieur interne : 000018 ( Pmc/Checkpoint ); précédent : 000017; suivant : 000019Semi-automated literature mining to identify putative biomarkers of disease from multiple biofluids
Auteurs : Rick Jordan [États-Unis] ; Shyam Visweswaran [États-Unis] ; Vanathi Gopalakrishnan [États-Unis]Source :
- Journal of Clinical Bioinformatics [ 2043-9113 ] ; 2014.
Abstract
Computational methods for mining of biomedical literature can be useful in augmenting manual searches of the literature using keywords for disease-specific biomarker discovery from biofluids. In this work, we develop and apply a semi-automated literature mining method to mine abstracts obtained from PubMed to discover putative biomarkers of breast and lung cancers in specific biofluids.
A positive set of abstracts was defined by the terms ‘breast cancer’ and ‘lung cancer’ in conjunction with 14 separate ‘biofluids’ (bile, blood, breastmilk, cerebrospinal fluid, mucus, plasma, saliva, semen, serum, synovial fluid, stool, sweat, tears, and urine), while a negative set of abstracts was defined by the terms ‘(biofluid) NOT breast cancer’ or ‘(biofluid) NOT lung cancer.’ More than 5.3 million total abstracts were obtained from PubMed and examined for biomarker-disease-biofluid associations (34,296 positive and 2,653,396 negative for breast cancer; 28,355 positive and 2,595,034 negative for lung cancer). Biological entities such as genes and proteins were tagged using ABNER, and processed using Python scripts to produce a list of putative biomarkers. Z-scores were calculated, ranked, and used to determine significance of putative biomarkers found. Manual verification of relevant abstracts was performed to assess our method’s performance.
Biofluid-specific markers were identified from the literature, assigned relevance scores based on frequency of occurrence, and validated using known biomarker lists and/or databases for lung and breast cancer [NCBI’s On-line Mendelian Inheritance in Man (OMIM), Cancer Gene annotation server for cancer genomics (CAGE), NCBI’s Genes & Disease, NCI’s Early Detection Research Network (EDRN), and others]. The specificity of each marker for a given biofluid was calculated, and the performance of our semi-automated literature mining method assessed for breast and lung cancer.
We developed a semi-automated process for determining a list of putative biomarkers for breast and lung cancer. New knowledge is presented in the form of biomarker lists; ranked, newly discovered biomarker-disease-biofluid relationships; and biomarker specificity across biofluids.
Url:
DOI: 10.1186/2043-9113-4-13
PubMed: 25379168
PubMed Central: 4215335
Affiliations:
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sortKey="Jordan, Rick" sort="Jordan, Rick" uniqKey="Jordan R" first="Rick" last="Jordan">Rick Jordan</name><affiliation wicri:level="4"><nlm:aff id="I1">Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA</wicri:regionArea><placeName><region type="state">Pennsylvanie</region><settlement type="city">Pittsburgh</settlement></placeName><orgName type="university">Université de Pittsburgh</orgName></affiliation></author><author><name sortKey="Visweswaran, Shyam" sort="Visweswaran, Shyam" uniqKey="Visweswaran S" first="Shyam" last="Visweswaran">Shyam Visweswaran</name><affiliation wicri:level="4"><nlm:aff id="I1">Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA</wicri:regionArea><placeName><region type="state">Pennsylvanie</region><settlement type="city">Pittsburgh</settlement></placeName><orgName type="university">Université de Pittsburgh</orgName></affiliation><affiliation wicri:level="4"><nlm:aff id="I2">Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA</wicri:regionArea><placeName><region type="state">Pennsylvanie</region><settlement type="city">Pittsburgh</settlement></placeName><orgName type="university">Université de Pittsburgh</orgName></affiliation><affiliation wicri:level="4"><nlm:aff id="I3">Department of Computational & Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Computational & Systems Biology, University of Pittsburgh, Pittsburgh, PA</wicri:regionArea><placeName><region type="state">Pennsylvanie</region><settlement type="city">Pittsburgh</settlement></placeName><orgName type="university">Université de Pittsburgh</orgName></affiliation></author><author><name sortKey="Gopalakrishnan, Vanathi" sort="Gopalakrishnan, Vanathi" uniqKey="Gopalakrishnan V" first="Vanathi" last="Gopalakrishnan">Vanathi Gopalakrishnan</name><affiliation wicri:level="4"><nlm:aff id="I1">Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA</wicri:regionArea><placeName><region type="state">Pennsylvanie</region><settlement type="city">Pittsburgh</settlement></placeName><orgName type="university">Université de Pittsburgh</orgName></affiliation><affiliation wicri:level="4"><nlm:aff id="I2">Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA</wicri:regionArea><placeName><region type="state">Pennsylvanie</region><settlement type="city">Pittsburgh</settlement></placeName><orgName type="university">Université de Pittsburgh</orgName></affiliation><affiliation wicri:level="4"><nlm:aff id="I3">Department of Computational & Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Computational & Systems Biology, University of Pittsburgh, Pittsburgh, PA</wicri:regionArea><placeName><region type="state">Pennsylvanie</region><settlement type="city">Pittsburgh</settlement></placeName><orgName type="university">Université de Pittsburgh</orgName></affiliation></author></analytic><series><title level="j">Journal of Clinical Bioinformatics</title><idno type="eISSN">2043-9113</idno><imprint><date when="2014">2014</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><sec><title>Background</title><p>Computational methods for mining of biomedical literature can be useful in augmenting manual searches of the literature using keywords for disease-specific biomarker discovery from biofluids. In this work, we develop and apply a semi-automated literature mining method to mine abstracts obtained from PubMed to discover putative biomarkers of breast and lung cancers in specific biofluids.</p></sec><sec><title>Methodology</title><p>A positive set of abstracts was defined by the terms ‘breast cancer’ and ‘lung cancer’ in conjunction with 14 separate ‘biofluids’ (bile, blood, breastmilk, cerebrospinal fluid, mucus, plasma, saliva, semen, serum, synovial fluid, stool, sweat, tears, and urine), while a negative set of abstracts was defined by the terms ‘(biofluid) NOT breast cancer’ or ‘(biofluid) NOT lung cancer.’ More than 5.3 million total abstracts were obtained from PubMed and examined for biomarker-disease-biofluid associations (34,296 positive and 2,653,396 negative for breast cancer; 28,355 positive and 2,595,034 negative for lung cancer). Biological entities such as genes and proteins were tagged using ABNER, and processed using Python scripts to produce a list of putative biomarkers. Z-scores were calculated, ranked, and used to determine significance of putative biomarkers found. Manual verification of relevant abstracts was performed to assess our method’s performance.</p></sec><sec><title>Results</title><p>Biofluid-specific markers were identified from the literature, assigned relevance scores based on frequency of occurrence, and validated using known biomarker lists and/or databases for lung and breast cancer [NCBI’s On-line Mendelian Inheritance in Man (OMIM), Cancer Gene annotation server for cancer genomics (CAGE), NCBI’s Genes & Disease, NCI’s Early Detection Research Network (EDRN), and others]. The specificity of each marker for a given biofluid was calculated, and the performance of our semi-automated literature mining method assessed for breast and lung cancer.</p></sec><sec><title>Conclusions</title><p>We developed a semi-automated process for determining a list of putative biomarkers for breast and lung cancer. New knowledge is presented in the form of biomarker lists; ranked, newly discovered biomarker-disease-biofluid relationships; and biomarker specificity across biofluids.</p></sec></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Hirschman, L" uniqKey="Hirschman L">L Hirschman</name></author><author><name sortKey="Park, Jc" uniqKey="Park J">JC Park</name></author><author><name sortKey="Tsujii, J" uniqKey="Tsujii J">J Tsujii</name></author><author><name sortKey="Wong, L" uniqKey="Wong L">L Wong</name></author><author><name sortKey="Wu, Ch" uniqKey="Wu C">CH Wu</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Adamic, La" uniqKey="Adamic L">LA Adamic</name></author><author><name sortKey="Wilkinson, D" uniqKey="Wilkinson D">D Wilkinson</name></author><author><name sortKey="Huberman, Ba" uniqKey="Huberman B">BA Huberman</name></author><author><name sortKey="Adar, E" uniqKey="Adar E">E 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<front><journal-meta><journal-id journal-id-type="nlm-ta">J Clin Bioinforma</journal-id><journal-id journal-id-type="iso-abbrev">J Clin Bioinforma</journal-id><journal-title-group><journal-title>Journal of Clinical Bioinformatics</journal-title></journal-title-group><issn pub-type="epub">2043-9113</issn><publisher><publisher-name>BioMed Central</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">25379168</article-id><article-id pub-id-type="pmc">4215335</article-id><article-id pub-id-type="publisher-id">2043-9113-4-13</article-id><article-id pub-id-type="doi">10.1186/2043-9113-4-13</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research</subject></subj-group></article-categories><title-group><article-title>Semi-automated literature mining to identify putative biomarkers of disease from multiple biofluids</article-title></title-group><contrib-group><contrib contrib-type="author" corresp="yes" id="A1"><name><surname>Jordan</surname><given-names>Rick</given-names></name><xref ref-type="aff" rid="I1">1</xref><email>rmj12@pitt.edu</email></contrib><contrib contrib-type="author" id="A2"><name><surname>Visweswaran</surname><given-names>Shyam</given-names></name><xref ref-type="aff" rid="I1">1</xref><xref ref-type="aff" rid="I2">2</xref><xref ref-type="aff" rid="I3">3</xref><email>shv3@pitt.edu</email></contrib><contrib contrib-type="author" id="A3"><name><surname>Gopalakrishnan</surname><given-names>Vanathi</given-names></name><xref ref-type="aff" rid="I1">1</xref><xref ref-type="aff" rid="I2">2</xref><xref ref-type="aff" rid="I3">3</xref><email>vanathi@pitt.edu</email></contrib></contrib-group><aff id="I1"><label>1</label>Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA</aff><aff id="I2"><label>2</label>Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, USA</aff><aff id="I3"><label>3</label>Department of Computational & Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA</aff><pub-date pub-type="collection"><year>2014</year></pub-date><pub-date pub-type="epub"><day>23</day><month>10</month><year>2014</year></pub-date><volume>4</volume><fpage>13</fpage><lpage>13</lpage><history><date date-type="received"><day>26</day><month>6</month><year>2014</year></date><date date-type="accepted"><day>2</day><month>10</month><year>2014</year></date></history><permissions><copyright-statement>Copyright © 2014 Jordan et al.; licensee BioMed Central Ltd.</copyright-statement><copyright-year>2014</copyright-year><copyright-holder>Jordan et al.; licensee BioMed Central Ltd.</copyright-holder><license license-type="open-access" xlink:href="http://creativecommons.org/licenses/by/4.0"><license-p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0">http://creativecommons.org/licenses/by/4.0</ext-link>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/publicdomain/zero/1.0/">http://creativecommons.org/publicdomain/zero/1.0/</ext-link>) applies to the data made available in this article, unless otherwise stated.</license-p></license></permissions><self-uri xlink:href="http://www.jclinbioinformatics.com/content/4/1/13"></self-uri><abstract><sec><title>Background</title><p>Computational methods for mining of biomedical literature can be useful in augmenting manual searches of the literature using keywords for disease-specific biomarker discovery from biofluids. In this work, we develop and apply a semi-automated literature mining method to mine abstracts obtained from PubMed to discover putative biomarkers of breast and lung cancers in specific biofluids.</p></sec><sec><title>Methodology</title><p>A positive set of abstracts was defined by the terms ‘breast cancer’ and ‘lung cancer’ in conjunction with 14 separate ‘biofluids’ (bile, blood, breastmilk, cerebrospinal fluid, mucus, plasma, saliva, semen, serum, synovial fluid, stool, sweat, tears, and urine), while a negative set of abstracts was defined by the terms ‘(biofluid) NOT breast cancer’ or ‘(biofluid) NOT lung cancer.’ More than 5.3 million total abstracts were obtained from PubMed and examined for biomarker-disease-biofluid associations (34,296 positive and 2,653,396 negative for breast cancer; 28,355 positive and 2,595,034 negative for lung cancer). Biological entities such as genes and proteins were tagged using ABNER, and processed using Python scripts to produce a list of putative biomarkers. Z-scores were calculated, ranked, and used to determine significance of putative biomarkers found. Manual verification of relevant abstracts was performed to assess our method’s performance.</p></sec><sec><title>Results</title><p>Biofluid-specific markers were identified from the literature, assigned relevance scores based on frequency of occurrence, and validated using known biomarker lists and/or databases for lung and breast cancer [NCBI’s On-line Mendelian Inheritance in Man (OMIM), Cancer Gene annotation server for cancer genomics (CAGE), NCBI’s Genes & Disease, NCI’s Early Detection Research Network (EDRN), and others]. The specificity of each marker for a given biofluid was calculated, and the performance of our semi-automated literature mining method assessed for breast and lung cancer.</p></sec><sec><title>Conclusions</title><p>We developed a semi-automated process for determining a list of putative biomarkers for breast and lung cancer. New knowledge is presented in the form of biomarker lists; ranked, newly discovered biomarker-disease-biofluid relationships; and biomarker specificity across biofluids.</p></sec></abstract><kwd-group><kwd>Literature mining</kwd><kwd>Text mining</kwd><kwd>Lung cancer</kwd><kwd>Breast cancer</kwd><kwd>Biomarker</kwd><kwd>Biofluid</kwd></kwd-group></article-meta></front></pmc><affiliations><list><country><li>États-Unis</li></country><region><li>Pennsylvanie</li></region><settlement><li>Pittsburgh</li></settlement><orgName><li>Université de Pittsburgh</li></orgName></list><tree><country name="États-Unis"><region name="Pennsylvanie"><name sortKey="Jordan, Rick" sort="Jordan, Rick" uniqKey="Jordan R" first="Rick" last="Jordan">Rick Jordan</name></region><name sortKey="Gopalakrishnan, Vanathi" sort="Gopalakrishnan, Vanathi" uniqKey="Gopalakrishnan V" first="Vanathi" last="Gopalakrishnan">Vanathi Gopalakrishnan</name><name sortKey="Gopalakrishnan, Vanathi" sort="Gopalakrishnan, Vanathi" uniqKey="Gopalakrishnan V" first="Vanathi" last="Gopalakrishnan">Vanathi Gopalakrishnan</name><name sortKey="Gopalakrishnan, Vanathi" sort="Gopalakrishnan, Vanathi" uniqKey="Gopalakrishnan V" first="Vanathi" last="Gopalakrishnan">Vanathi Gopalakrishnan</name><name sortKey="Visweswaran, Shyam" sort="Visweswaran, Shyam" uniqKey="Visweswaran S" first="Shyam" last="Visweswaran">Shyam Visweswaran</name><name sortKey="Visweswaran, Shyam" sort="Visweswaran, Shyam" uniqKey="Visweswaran S" first="Shyam" last="Visweswaran">Shyam Visweswaran</name><name sortKey="Visweswaran, Shyam" sort="Visweswaran, Shyam" uniqKey="Visweswaran S" first="Shyam" last="Visweswaran">Shyam Visweswaran</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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