The analysis of disease biomarker data using a mixed hidden Markov model (Open Access publication)
Identifieur interne : 000075 ( PascalFrancis/Corpus ); précédent : 000074; suivant : 000076The analysis of disease biomarker data using a mixed hidden Markov model (Open Access publication)
Auteurs : Johann C. DetilleuxSource :
- Genetics selection evolution : (Print) [ 0999-193X ] ; 2008.
Descripteurs français
- Pascal (Inist)
English descriptors
- KwdEn :
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.
Notice en format standard (ISO 2709)
Pour connaître la documentation sur le format Inist Standard.
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Format Inist (serveur)
NO : | PASCAL 08-0448742 INIST |
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ET : | The analysis of disease biomarker data using a mixed hidden Markov model (Open Access publication) |
AU : | DETILLEUX (Johann C.) |
AF : | Quantitative Genetics Group, Department of Animal Production, Faculty of Veterinary Medicine, University of Liege/Liège/Belgique (1 aut.) |
DT : | Publication en série; Niveau analytique |
SO : | Genetics selection evolution : (Print); ISSN 0999-193X; France; Da. 2008; Vol. 40; No. 5; Pp. 491-509; Bibl. 22 ref. |
LA : | Anglais |
EA : | 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. |
CC : | 002A07C01B; 002A36C03 |
FD : | Maladie; Marqueur biologique; Analyse donnée; Modèle Markov caché; Modèle mixte; Utilisation; Bovin laitier; Vache; Cellule somatique; Taux; Mastite; Valeur génétique; Prédiction; Morbidité; Santé; Probabilité |
FG : | Artiodactyla; Ungulata; Mammalia; Vertebrata; Boeuf; Animal élevage; Animal ruminant; Pathologie de la glande mammaire; Méthode statistique; Génétique quantitative; Médecine vétérinaire; Sélection dirigée; Zootechnie; Animal laitier; Bovin; Herbivore; Femelle; Biomathématique; Sciences animales; Sélection génétique |
ED : | Disease; Biological marker; Data analysis; Hidden Markov model; Mixed model; Use; Dairy cattle; Cow; Somatic cell; Rate; Mastitis; Breeding value; Prediction; Morbidity; Health; Probability |
EG : | Artiodactyla; Ungulata; Mammalia; Vertebrata; Ox; Farming animal; Ruminant animal; Mammary gland diseases; Statistical method; Quantitative genetics; Veterinary medicine; Artificial selection; Zootechny |
SD : | Enfermedad; Marcador biológico; Análisis datos; Modelo Markov oculto; Modelo mixto; Uso; Ganado de leche; Vaca; Célula somática; Tasa; Mastitis; Valor genético; Predicción; Morbilidad; Salud; Probabilidad |
LO : | INIST-14418.354000197485090020 |
ID : | 08-0448742 |
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Pascal:08-0448742Le document en format XML
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<term>Utilisation</term>
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<term>Vache</term>
<term>Cellule somatique</term>
<term>Taux</term>
<term>Mastite</term>
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<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>
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<s5>04</s5>
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<s5>05</s5>
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<s5>05</s5>
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<s5>05</s5>
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<s5>06</s5>
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<s5>06</s5>
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<s5>07</s5>
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<s5>07</s5>
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<s5>07</s5>
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<s5>08</s5>
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<s5>09</s5>
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<s5>09</s5>
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<s5>10</s5>
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<s5>10</s5>
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<s5>11</s5>
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<s5>11</s5>
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<s5>11</s5>
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<s5>12</s5>
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<s5>12</s5>
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<s5>12</s5>
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<s5>13</s5>
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<s5>13</s5>
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<s5>13</s5>
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<s5>17</s5>
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<s5>17</s5>
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<s5>18</s5>
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<s5>18</s5>
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<s5>19</s5>
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<fC03 i1="16" i2="X" l="ENG"><s0>Probability</s0>
<s5>19</s5>
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<s5>19</s5>
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<s2>NS</s2>
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<s2>NS</s2>
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<s2>NS</s2>
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<s2>NS</s2>
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<s2>NS</s2>
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<s2>NS</s2>
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<s5>32</s5>
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<s5>32</s5>
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<s5>32</s5>
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<s5>33</s5>
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<s2>NM</s2>
<s5>33</s5>
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<s5>35</s5>
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<s5>36</s5>
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<s5>36</s5>
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<s5>36</s5>
</fC07>
<fC07 i1="12" i2="X" l="FRE"><s0>Sélection dirigée</s0>
<s5>37</s5>
</fC07>
<fC07 i1="12" i2="X" l="ENG"><s0>Artificial selection</s0>
<s5>37</s5>
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<fC07 i1="12" i2="X" l="SPA"><s0>Selección dirigida</s0>
<s5>37</s5>
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<fC07 i1="13" i2="X" l="FRE"><s0>Zootechnie</s0>
<s5>38</s5>
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<fC07 i1="13" i2="X" l="ENG"><s0>Zootechny</s0>
<s5>38</s5>
</fC07>
<fC07 i1="13" i2="X" l="SPA"><s0>Zootecnia</s0>
<s5>38</s5>
</fC07>
<fC07 i1="14" i2="X" l="FRE"><s0>Animal laitier</s0>
<s4>INC</s4>
<s5>81</s5>
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<fC07 i1="15" i2="X" l="FRE"><s0>Bovin</s0>
<s4>INC</s4>
<s5>82</s5>
</fC07>
<fC07 i1="16" i2="X" l="FRE"><s0>Herbivore</s0>
<s4>INC</s4>
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<server><NO>PASCAL 08-0448742 INIST</NO>
<ET>The analysis of disease biomarker data using a mixed hidden Markov model (Open Access publication)</ET>
<AU>DETILLEUX (Johann C.)</AU>
<AF>Quantitative Genetics Group, Department of Animal Production, Faculty of Veterinary Medicine, University of Liege/Liège/Belgique (1 aut.)</AF>
<DT>Publication en série; Niveau analytique</DT>
<SO>Genetics selection evolution : (Print); ISSN 0999-193X; France; Da. 2008; Vol. 40; No. 5; Pp. 491-509; Bibl. 22 ref.</SO>
<LA>Anglais</LA>
<EA>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.</EA>
<CC>002A07C01B; 002A36C03</CC>
<FD>Maladie; Marqueur biologique; Analyse donnée; Modèle Markov caché; Modèle mixte; Utilisation; Bovin laitier; Vache; Cellule somatique; Taux; Mastite; Valeur génétique; Prédiction; Morbidité; Santé; Probabilité</FD>
<FG>Artiodactyla; Ungulata; Mammalia; Vertebrata; Boeuf; Animal élevage; Animal ruminant; Pathologie de la glande mammaire; Méthode statistique; Génétique quantitative; Médecine vétérinaire; Sélection dirigée; Zootechnie; Animal laitier; Bovin; Herbivore; Femelle; Biomathématique; Sciences animales; Sélection génétique</FG>
<ED>Disease; Biological marker; Data analysis; Hidden Markov model; Mixed model; Use; Dairy cattle; Cow; Somatic cell; Rate; Mastitis; Breeding value; Prediction; Morbidity; Health; Probability</ED>
<EG>Artiodactyla; Ungulata; Mammalia; Vertebrata; Ox; Farming animal; Ruminant animal; Mammary gland diseases; Statistical method; Quantitative genetics; Veterinary medicine; Artificial selection; Zootechny</EG>
<SD>Enfermedad; Marcador biológico; Análisis datos; Modelo Markov oculto; Modelo mixto; Uso; Ganado de leche; Vaca; Célula somática; Tasa; Mastitis; Valor genético; Predicción; Morbilidad; Salud; Probabilidad</SD>
<LO>INIST-14418.354000197485090020</LO>
<ID>08-0448742</ID>
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