The analysis of disease biomarker data using a mixed hidden Markov model (Open Access publication).
Identifieur interne : 000053 ( PubMed/Corpus ); précédent : 000052; suivant : 000054The analysis of disease biomarker data using a mixed hidden Markov model (Open Access publication).
Auteurs : Johann C. DetilleuxSource :
- Genetics, selection, evolution : GSE [ 0999-193X ]
English descriptors
- KwdEn :
- Animals, Biomarkers (analysis), Cattle (genetics), Computer Simulation, Dairying, Escherichia coli Infections (genetics), Female, Genetic Predisposition to Disease, Lactation (genetics), Markov Chains, Mastitis, Bovine (genetics), Models, Theoretical, Staphylococcal Infections (genetics), Staphylococcus aureus (physiology).
- MESH :
- chemical , analysis : Biomarkers.
- genetics : Cattle, Escherichia coli Infections, Lactation, Mastitis, Bovine, Staphylococcal Infections.
- physiology : Staphylococcus aureus.
- Animals, Computer Simulation, Dairying, Female, Genetic Predisposition to Disease, Markov Chains, Models, Theoretical.
Abstract
A mixed hidden Markov model (HMM) was developed for predicting breeding values of a biomarker (here, somatic cell score) and the individual probabilities of health and disease (here, mastitis) based upon the measurements of the biomarker. At a first level, the unobserved disease process (Markov model) was introduced and at a second level, the measurement process was modeled, making the link between the unobserved disease states and the observed biomarker values. This hierarchical formulation allows joint estimation of the parameters of both processes. The flexibility of this approach is illustrated on the simulated data. Firstly, lactation curves for the biomarker were generated based upon published parameters (mean, variance, and probabilities of infection) for cows with known clinical conditions (health or mastitis due to Escherichia coli or Staphylococcus aureus). Next, estimation of the parameters was performed via Gibbs sampling, assuming the health status was unknown. Results from the simulations and mathematics show that the mixed HMM is appropriate to estimate the quantities of interest although the accuracy of the estimates is moderate when the prevalence of the disease is low. The paper ends with some indications for further developments of the methodology.
DOI: 10.1051/gse:2008017
PubMed: 18694546
Links to Exploration step
pubmed:18694546Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">The analysis of disease biomarker data using a mixed hidden Markov model (Open Access publication).</title>
<author><name sortKey="Detilleux, Johann C" sort="Detilleux, Johann C" uniqKey="Detilleux J" first="Johann C" last="Detilleux">Johann C. Detilleux</name>
<affiliation><nlm:affiliation>Quantitative Genetics Group, Department of Animal Production, Faculty of Veterinary Medicine, University of Liège, Liège, Belgium. jdetilleux@ulg.ac.be</nlm:affiliation>
</affiliation>
</author>
</titleStmt>
<publicationStmt><idno type="wicri:source">PubMed</idno>
<date when="????"><PubDate><MedlineDate>2008 Sep-Oct</MedlineDate>
</PubDate>
</date>
<idno type="RBID">pubmed:18694546</idno>
<idno type="pmid">18694546</idno>
<idno type="doi">10.1051/gse:2008017</idno>
<idno type="wicri:Area/PubMed/Corpus">000053</idno>
</publicationStmt>
<sourceDesc><biblStruct><analytic><title xml:lang="en">The analysis of disease biomarker data using a mixed hidden Markov model (Open Access publication).</title>
<author><name sortKey="Detilleux, Johann C" sort="Detilleux, Johann C" uniqKey="Detilleux J" first="Johann C" last="Detilleux">Johann C. Detilleux</name>
<affiliation><nlm:affiliation>Quantitative Genetics Group, Department of Animal Production, Faculty of Veterinary Medicine, University of Liège, Liège, Belgium. jdetilleux@ulg.ac.be</nlm:affiliation>
</affiliation>
</author>
</analytic>
<series><title level="j">Genetics, selection, evolution : GSE</title>
<idno type="ISSN">0999-193X</idno>
</series>
</biblStruct>
</sourceDesc>
</fileDesc>
<profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Animals</term>
<term>Biomarkers (analysis)</term>
<term>Cattle (genetics)</term>
<term>Computer Simulation</term>
<term>Dairying</term>
<term>Escherichia coli Infections (genetics)</term>
<term>Female</term>
<term>Genetic Predisposition to Disease</term>
<term>Lactation (genetics)</term>
<term>Markov Chains</term>
<term>Mastitis, Bovine (genetics)</term>
<term>Models, Theoretical</term>
<term>Staphylococcal Infections (genetics)</term>
<term>Staphylococcus aureus (physiology)</term>
</keywords>
<keywords scheme="MESH" type="chemical" qualifier="analysis" xml:lang="en"><term>Biomarkers</term>
</keywords>
<keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>Cattle</term>
<term>Escherichia coli Infections</term>
<term>Lactation</term>
<term>Mastitis, Bovine</term>
<term>Staphylococcal Infections</term>
</keywords>
<keywords scheme="MESH" qualifier="physiology" xml:lang="en"><term>Staphylococcus aureus</term>
</keywords>
<keywords scheme="MESH" xml:lang="en"><term>Animals</term>
<term>Computer Simulation</term>
<term>Dairying</term>
<term>Female</term>
<term>Genetic Predisposition to Disease</term>
<term>Markov Chains</term>
<term>Models, Theoretical</term>
</keywords>
</textClass>
</profileDesc>
</teiHeader>
<front><div type="abstract" xml:lang="en">A mixed hidden Markov model (HMM) was developed for predicting breeding values of a biomarker (here, somatic cell score) and the individual probabilities of health and disease (here, mastitis) based upon the measurements of the biomarker. At a first level, the unobserved disease process (Markov model) was introduced and at a second level, the measurement process was modeled, making the link between the unobserved disease states and the observed biomarker values. This hierarchical formulation allows joint estimation of the parameters of both processes. The flexibility of this approach is illustrated on the simulated data. Firstly, lactation curves for the biomarker were generated based upon published parameters (mean, variance, and probabilities of infection) for cows with known clinical conditions (health or mastitis due to Escherichia coli or Staphylococcus aureus). Next, estimation of the parameters was performed via Gibbs sampling, assuming the health status was unknown. Results from the simulations and mathematics show that the mixed HMM is appropriate to estimate the quantities of interest although the accuracy of the estimates is moderate when the prevalence of the disease is low. The paper ends with some indications for further developments of the methodology.</div>
</front>
</TEI>
<pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">18694546</PMID>
<DateCreated><Year>2008</Year>
<Month>8</Month>
<Day>12</Day>
</DateCreated>
<DateCompleted><Year>2008</Year>
<Month>11</Month>
<Day>17</Day>
</DateCompleted>
<DateRevised><Year>2015</Year>
<Month>11</Month>
<Day>19</Day>
</DateRevised>
<Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Print">0999-193X</ISSN>
<JournalIssue CitedMedium="Print"><Volume>40</Volume>
<Issue>5</Issue>
<PubDate><MedlineDate>2008 Sep-Oct</MedlineDate>
</PubDate>
</JournalIssue>
<Title>Genetics, selection, evolution : GSE</Title>
<ISOAbbreviation>Genet. Sel. Evol.</ISOAbbreviation>
</Journal>
<ArticleTitle>The analysis of disease biomarker data using a mixed hidden Markov model (Open Access publication).</ArticleTitle>
<Pagination><MedlinePgn>491-509</MedlinePgn>
</Pagination>
<ELocationID EIdType="doi" ValidYN="Y">10.1051/gse:2008017</ELocationID>
<Abstract><AbstractText>A mixed hidden Markov model (HMM) was developed for predicting breeding values of a biomarker (here, somatic cell score) and the individual probabilities of health and disease (here, mastitis) based upon the measurements of the biomarker. At a first level, the unobserved disease process (Markov model) was introduced and at a second level, the measurement process was modeled, making the link between the unobserved disease states and the observed biomarker values. This hierarchical formulation allows joint estimation of the parameters of both processes. The flexibility of this approach is illustrated on the simulated data. Firstly, lactation curves for the biomarker were generated based upon published parameters (mean, variance, and probabilities of infection) for cows with known clinical conditions (health or mastitis due to Escherichia coli or Staphylococcus aureus). Next, estimation of the parameters was performed via Gibbs sampling, assuming the health status was unknown. Results from the simulations and mathematics show that the mixed HMM is appropriate to estimate the quantities of interest although the accuracy of the estimates is moderate when the prevalence of the disease is low. The paper ends with some indications for further developments of the methodology.</AbstractText>
</Abstract>
<AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Detilleux</LastName>
<ForeName>Johann C</ForeName>
<Initials>JC</Initials>
<AffiliationInfo><Affiliation>Quantitative Genetics Group, Department of Animal Production, Faculty of Veterinary Medicine, University of Liège, Liège, Belgium. jdetilleux@ulg.ac.be</Affiliation>
</AffiliationInfo>
</Author>
</AuthorList>
<Language>ENG</Language>
<PublicationTypeList><PublicationType UI="D023362">Evaluation Studies</PublicationType>
<PublicationType UI="D016428">Journal Article</PublicationType>
<PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType>
</PublicationTypeList>
<ArticleDate DateType="Electronic"><Year>2008</Year>
<Month>Aug</Month>
<Day>12</Day>
</ArticleDate>
</Article>
<MedlineJournalInfo><Country>France</Country>
<MedlineTA>Genet Sel Evol</MedlineTA>
<NlmUniqueID>9114088</NlmUniqueID>
<ISSNLinking>0999-193X</ISSNLinking>
</MedlineJournalInfo>
<ChemicalList><Chemical><RegistryNumber>0</RegistryNumber>
<NameOfSubstance UI="D015415">Biomarkers</NameOfSubstance>
</Chemical>
</ChemicalList>
<CitationSubset>IM</CitationSubset>
<MeshHeadingList><MeshHeading><DescriptorName UI="D000818" MajorTopicYN="N">Animals</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D015415" MajorTopicYN="N">Biomarkers</DescriptorName>
<QualifierName UI="Q000032" MajorTopicYN="Y">analysis</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D002417" MajorTopicYN="N">Cattle</DescriptorName>
<QualifierName UI="Q000235" MajorTopicYN="Y">genetics</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D003198" MajorTopicYN="N">Computer Simulation</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D003612" MajorTopicYN="N">Dairying</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D004927" MajorTopicYN="N">Escherichia coli Infections</DescriptorName>
<QualifierName UI="Q000235" MajorTopicYN="N">genetics</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D005260" MajorTopicYN="N">Female</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D020022" MajorTopicYN="N">Genetic Predisposition to Disease</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D007774" MajorTopicYN="N">Lactation</DescriptorName>
<QualifierName UI="Q000235" MajorTopicYN="N">genetics</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D008390" MajorTopicYN="Y">Markov Chains</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D008414" MajorTopicYN="N">Mastitis, Bovine</DescriptorName>
<QualifierName UI="Q000235" MajorTopicYN="Y">genetics</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D008962" MajorTopicYN="N">Models, Theoretical</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D013203" MajorTopicYN="N">Staphylococcal Infections</DescriptorName>
<QualifierName UI="Q000235" MajorTopicYN="N">genetics</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D013211" MajorTopicYN="N">Staphylococcus aureus</DescriptorName>
<QualifierName UI="Q000502" MajorTopicYN="N">physiology</QualifierName>
</MeshHeading>
</MeshHeadingList>
<OtherID Source="NLM">PMC2674886</OtherID>
</MedlineCitation>
<PubmedData><History><PubMedPubDate PubStatus="received"><Year>2007</Year>
<Month>9</Month>
<Day>13</Day>
</PubMedPubDate>
<PubMedPubDate PubStatus="accepted"><Year>2008</Year>
<Month>3</Month>
<Day>3</Day>
</PubMedPubDate>
<PubMedPubDate PubStatus="pubmed"><Year>2008</Year>
<Month>8</Month>
<Day>13</Day>
<Hour>9</Hour>
<Minute>0</Minute>
</PubMedPubDate>
<PubMedPubDate PubStatus="medline"><Year>2008</Year>
<Month>11</Month>
<Day>18</Day>
<Hour>9</Hour>
<Minute>0</Minute>
</PubMedPubDate>
<PubMedPubDate PubStatus="entrez"><Year>2008</Year>
<Month>8</Month>
<Day>13</Day>
<Hour>9</Hour>
<Minute>0</Minute>
</PubMedPubDate>
</History>
<PublicationStatus>ppublish</PublicationStatus>
<ArticleIdList><ArticleId IdType="pubmed">18694546</ArticleId>
<ArticleId IdType="doi">10.1051/gse:2008017</ArticleId>
<ArticleId IdType="pii">g07084</ArticleId>
<ArticleId IdType="pmc">PMC2674886</ArticleId>
</ArticleIdList>
</PubmedData>
</pubmed>
</record>
Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Wicri/Belgique/explor/OpenAccessBelV2/Data/PubMed/Corpus
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 000053 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/PubMed/Corpus/biblio.hfd -nk 000053 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien |wiki= Wicri/Belgique |area= OpenAccessBelV2 |flux= PubMed |étape= Corpus |type= RBID |clé= pubmed:18694546 |texte= The analysis of disease biomarker data using a mixed hidden Markov model (Open Access publication). }}
Pour générer des pages wiki
HfdIndexSelect -h $EXPLOR_AREA/Data/PubMed/Corpus/RBID.i -Sk "pubmed:18694546" \ | HfdSelect -Kh $EXPLOR_AREA/Data/PubMed/Corpus/biblio.hfd \ | NlmPubMed2Wicri -a OpenAccessBelV2
![]() | This area was generated with Dilib version V0.6.25. | ![]() |