The analysis of disease biomarker data using a mixed hidden Markov model (Open Access publication)
Identifieur interne : 000112 ( OpenAccess/Analysis ); précédent : 000111; suivant : 000113The analysis of disease biomarker data using a mixed hidden Markov model (Open Access publication)
Auteurs : Johann C. Detilleux [Belgique]Source :
- Genetics selection evolution : (Print) [ 0999-193X ] ; 2008.
Descripteurs français
- Pascal (Inist)
- Wicri :
English descriptors
- KwdEn :
- Animals, Biological marker, Biomarkers (analysis), Breeding value, Cattle (genetics), Computer Simulation, Cow, Dairy cattle, Dairying, Data analysis, Disease, Escherichia coli Infections (genetics), Female, Genetic Predisposition to Disease, Health, Hidden Markov model, Lactation (genetics), Markov Chains, Mastitis, Mastitis, Bovine (genetics), Mixed model, Models, Theoretical, Morbidity, Prediction, Probability, Rate, Somatic cell, Staphylococcal Infections (genetics), Staphylococcus aureus (physiology), Use.
- 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.
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Pascal:08-0448742Le document en format XML
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Detilleux</name><affiliation wicri:level="1"><inist:fA14 i1="01"><s1>Quantitative Genetics Group, Department of Animal Production, Faculty of Veterinary Medicine, University of Liege</s1><s2>Liège</s2><s3>BEL</s3><sZ>1 aut.</sZ></inist:fA14><country>Belgique</country><wicri:noRegion>Liège</wicri:noRegion></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">INIST</idno><idno type="inist">08-0448742</idno><date when="2008">2008</date><idno type="stanalyst">PASCAL 08-0448742 INIST</idno><idno type="RBID">Pascal:08-0448742</idno><idno type="wicri:Area/PascalFrancis/Corpus">000075</idno><idno type="wicri:Area/PascalFrancis/Curation">000074</idno><idno type="wicri:Area/PascalFrancis/Checkpoint">000057</idno><idno type="wicri:doubleKey">0999-193X:2008:Detilleux J:the:analysis:of</idno><idno type="wicri:Area/Main/Merge">001422</idno><idno type="wicri:source">PMC</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2674886</idno><idno type="RBID">PMC:2674886</idno><idno type="wicri:Area/Pmc/Corpus">000237</idno><idno type="wicri:Area/Pmc/Curation">000237</idno><idno type="wicri:Area/Pmc/Checkpoint">000415</idno><idno type="wicri:source">PubMed</idno><idno type="wicri:Area/PubMed/Corpus">000053</idno><idno type="wicri:Area/PubMed/Curation">000053</idno><idno type="wicri:Area/PubMed/Checkpoint">000053</idno><idno type="wicri:Area/Ncbi/Merge">000039</idno><idno type="wicri:Area/Ncbi/Curation">000039</idno><idno type="wicri:Area/Ncbi/Checkpoint">000039</idno><idno type="wicri:doubleKey">0999-193X:2008:Detilleux J:the:analysis:of</idno><idno type="wicri:Area/Main/Merge">001272</idno><idno type="wicri:Area/Main/Curation">001419</idno><idno type="wicri:Area/Main/Exploration">001419</idno><idno type="wicri:Area/OpenAccess/Extraction">000112</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a">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 wicri:level="1"><inist:fA14 i1="01"><s1>Quantitative Genetics Group, Department of Animal Production, Faculty of Veterinary Medicine, University of Liege</s1><s2>Liège</s2><s3>BEL</s3><sZ>1 aut.</sZ></inist:fA14><country>Belgique</country><wicri:noRegion>Liège</wicri:noRegion></affiliation></author></analytic><series><title level="j" type="main">Genetics selection evolution : (Print)</title><title level="j" type="abbreviated">Genet. sel. evol. : (Print)</title><idno type="ISSN">0999-193X</idno><imprint><date when="2008">2008</date></imprint></series></biblStruct></sourceDesc><seriesStmt><title level="j" type="main">Genetics selection evolution : (Print)</title><title level="j" type="abbreviated">Genet. sel. evol. : (Print)</title><idno type="ISSN">0999-193X</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Animals</term><term>Biological marker</term><term>Biomarkers (analysis)</term><term>Breeding value</term><term>Cattle (genetics)</term><term>Computer Simulation</term><term>Cow</term><term>Dairy cattle</term><term>Dairying</term><term>Data analysis</term><term>Disease</term><term>Escherichia coli Infections (genetics)</term><term>Female</term><term>Genetic Predisposition to Disease</term><term>Health</term><term>Hidden Markov model</term><term>Lactation (genetics)</term><term>Markov Chains</term><term>Mastitis</term><term>Mastitis, Bovine (genetics)</term><term>Mixed model</term><term>Models, Theoretical</term><term>Morbidity</term><term>Prediction</term><term>Probability</term><term>Rate</term><term>Somatic cell</term><term>Staphylococcal Infections (genetics)</term><term>Staphylococcus aureus (physiology)</term><term>Use</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><keywords scheme="Pascal" xml:lang="fr"><term>Maladie</term><term>Marqueur biologique</term><term>Analyse donnée</term><term>Modèle Markov caché</term><term>Modèle mixte</term><term>Utilisation</term><term>Bovin laitier</term><term>Vache</term><term>Cellule somatique</term><term>Taux</term><term>Mastite</term><term>Valeur génétique</term><term>Prédiction</term><term>Morbidité</term><term>Santé</term><term>Probabilité</term></keywords><keywords scheme="Wicri" type="topic" xml:lang="fr"><term>Maladie</term><term>Vache</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><affiliations><list><country><li>Belgique</li></country></list><tree><country name="Belgique"><noRegion><name sortKey="Detilleux, Johann C" sort="Detilleux, Johann C" uniqKey="Detilleux J" first="Johann C." last="Detilleux">Johann C. Detilleux</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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