OpenAccessBelV2, PascalFrancis, Corpus, bibRecord, 000075

The analysis of disease biomarker data using a mixed hidden Markov model (Open Access publication)

Identifieur interne : 000075 ( PascalFrancis/Corpus ); précédent : 000074; suivant : 000076

The analysis of disease biomarker data using a mixed hidden Markov model (Open Access publication)

Auteurs : Johann C. Detilleux

Source :

Genetics selection evolution : (Print) [ 0999-193X ] ; 2008.

RBID : Pascal:08-0448742

Descripteurs français

Pascal (Inist)
- Maladie, Marqueur biologique, Analyse donnée, Modèle Markov caché, Modèle mixte, Utilisation, Bovin laitier, Vache, Cellule somatique, Taux, Mastite, Valeur génétique, Prédiction, Morbidité, Santé, Probabilité.

English descriptors

KwdEn :
- Biological marker, Breeding value, Cow, Dairy cattle, Data analysis, Disease, Health, Hidden Markov model, Mastitis, Mixed model, Morbidity, Prediction, Probability, Rate, Somatic cell, Use.

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.

A01	`01`	`1`		`@0 0999-193X`
A03		`1`		`@0 Genet. sel. evol. : (Print)`
A05				`@2 40`
A06				`@2 5`
A08	`01`	`1`	`ENG`	`@1 The analysis of disease biomarker data using a mixed hidden Markov model (Open Access publication)`
A11	`01`	`1`		`@1 DETILLEUX (Johann C.)`
A14	`01`			`@1 Quantitative Genetics Group, Department of Animal Production, Faculty of Veterinary Medicine, University of Liege @2 Liège @3 BEL @Z 1 aut.`
A20				`@1 491-509`
A21				`@1 2008`
A23	`01`			`@0 ENG`
A43	`01`			`@1 INIST @2 14418 @5 354000197485090020`
A44				`@0 0000 @1 © 2008 INIST-CNRS. All rights reserved.`
A45				`@0 22 ref.`
A47	`01`	`1`		`@0 08-0448742`
A60				`@1 P`
A61				`@0 A`
A64	`01`	`1`		`@0 Genetics selection evolution : (Print)`
A66	`01`			`@0 FRA`
C01	`01`		`ENG`	@0 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.
C02	`01`	`X`		`@0 002A07C01B`
C02	`02`	`X`		`@0 002A36C03`
C03	`01`	`X`	`FRE`	`@0 Maladie @5 01`
C03	`01`	`X`	`ENG`	`@0 Disease @5 01`
C03	`01`	`X`	`SPA`	`@0 Enfermedad @5 01`
C03	`02`	`X`	`FRE`	`@0 Marqueur biologique @5 02`
C03	`02`	`X`	`ENG`	`@0 Biological marker @5 02`
C03	`02`	`X`	`SPA`	`@0 Marcador biológico @5 02`
C03	`03`	`X`	`FRE`	`@0 Analyse donnée @5 03`
C03	`03`	`X`	`ENG`	`@0 Data analysis @5 03`
C03	`03`	`X`	`SPA`	`@0 Análisis datos @5 03`
C03	`04`	`X`	`FRE`	`@0 Modèle Markov caché @5 04`
C03	`04`	`X`	`ENG`	`@0 Hidden Markov model @5 04`
C03	`04`	`X`	`SPA`	`@0 Modelo Markov oculto @5 04`
C03	`05`	`X`	`FRE`	`@0 Modèle mixte @5 05`
C03	`05`	`X`	`ENG`	`@0 Mixed model @5 05`
C03	`05`	`X`	`SPA`	`@0 Modelo mixto @5 05`
C03	`06`	`X`	`FRE`	`@0 Utilisation @5 06`
C03	`06`	`X`	`ENG`	`@0 Use @5 06`
C03	`06`	`X`	`SPA`	`@0 Uso @5 06`
C03	`07`	`X`	`FRE`	`@0 Bovin laitier @5 07`
C03	`07`	`X`	`ENG`	`@0 Dairy cattle @5 07`
C03	`07`	`X`	`SPA`	`@0 Ganado de leche @5 07`
C03	`08`	`X`	`FRE`	`@0 Vache @5 08`
C03	`08`	`X`	`ENG`	`@0 Cow @5 08`
C03	`08`	`X`	`SPA`	`@0 Vaca @5 08`
C03	`09`	`X`	`FRE`	`@0 Cellule somatique @5 09`
C03	`09`	`X`	`ENG`	`@0 Somatic cell @5 09`
C03	`09`	`X`	`SPA`	`@0 Célula somática @5 09`
C03	`10`	`X`	`FRE`	`@0 Taux @5 10`
C03	`10`	`X`	`ENG`	`@0 Rate @5 10`
C03	`10`	`X`	`SPA`	`@0 Tasa @5 10`
C03	`11`	`X`	`FRE`	`@0 Mastite @5 11`
C03	`11`	`X`	`ENG`	`@0 Mastitis @5 11`
C03	`11`	`X`	`SPA`	`@0 Mastitis @5 11`
C03	`12`	`X`	`FRE`	`@0 Valeur génétique @5 12`
C03	`12`	`X`	`ENG`	`@0 Breeding value @5 12`
C03	`12`	`X`	`SPA`	`@0 Valor genético @5 12`
C03	`13`	`X`	`FRE`	`@0 Prédiction @5 13`
C03	`13`	`X`	`ENG`	`@0 Prediction @5 13`
C03	`13`	`X`	`SPA`	`@0 Predicción @5 13`
C03	`14`	`X`	`FRE`	`@0 Morbidité @5 17`
C03	`14`	`X`	`ENG`	`@0 Morbidity @5 17`
C03	`14`	`X`	`SPA`	`@0 Morbilidad @5 17`
C03	`15`	`X`	`FRE`	`@0 Santé @5 18`
C03	`15`	`X`	`ENG`	`@0 Health @5 18`
C03	`15`	`X`	`SPA`	`@0 Salud @5 18`
C03	`16`	`X`	`FRE`	`@0 Probabilité @5 19`
C03	`16`	`X`	`ENG`	`@0 Probability @5 19`
C03	`16`	`X`	`SPA`	`@0 Probabilidad @5 19`
C07	`01`	`X`	`FRE`	`@0 Artiodactyla @2 NS`
C07	`01`	`X`	`ENG`	`@0 Artiodactyla @2 NS`
C07	`01`	`X`	`SPA`	`@0 Artiodactyla @2 NS`
C07	`02`	`X`	`FRE`	`@0 Ungulata @2 NS`
C07	`02`	`X`	`ENG`	`@0 Ungulata @2 NS`
C07	`02`	`X`	`SPA`	`@0 Ungulata @2 NS`
C07	`03`	`X`	`FRE`	`@0 Mammalia @2 NS`
C07	`03`	`X`	`ENG`	`@0 Mammalia @2 NS`
C07	`03`	`X`	`SPA`	`@0 Mammalia @2 NS`
C07	`04`	`X`	`FRE`	`@0 Vertebrata @2 NS`
C07	`04`	`X`	`ENG`	`@0 Vertebrata @2 NS`
C07	`04`	`X`	`SPA`	`@0 Vertebrata @2 NS`
C07	`05`	`X`	`FRE`	`@0 Boeuf`
C07	`05`	`X`	`ENG`	`@0 Ox`
C07	`05`	`X`	`SPA`	`@0 Buey`
C07	`06`	`X`	`FRE`	`@0 Animal élevage @5 31`
C07	`06`	`X`	`ENG`	`@0 Farming animal @5 31`
C07	`06`	`X`	`SPA`	`@0 Animal cría @5 31`
C07	`07`	`X`	`FRE`	`@0 Animal ruminant @5 32`
C07	`07`	`X`	`ENG`	`@0 Ruminant animal @5 32`
C07	`07`	`X`	`SPA`	`@0 Animal rumiante @5 32`
C07	`08`	`X`	`FRE`	`@0 Pathologie de la glande mammaire @2 NM @5 33`
C07	`08`	`X`	`ENG`	`@0 Mammary gland diseases @2 NM @5 33`
C07	`08`	`X`	`SPA`	`@0 Glándula mamaria patología @2 NM @5 33`
C07	`09`	`X`	`FRE`	`@0 Méthode statistique @5 34`
C07	`09`	`X`	`ENG`	`@0 Statistical method @5 34`
C07	`09`	`X`	`SPA`	`@0 Método estadístico @5 34`
C07	`10`	`X`	`FRE`	`@0 Génétique quantitative @5 35`
C07	`10`	`X`	`ENG`	`@0 Quantitative genetics @5 35`
C07	`10`	`X`	`SPA`	`@0 Genética cuantitativa @5 35`
C07	`11`	`X`	`FRE`	`@0 Médecine vétérinaire @5 36`
C07	`11`	`X`	`ENG`	`@0 Veterinary medicine @5 36`
C07	`11`	`X`	`SPA`	`@0 Medicina veterinaria @5 36`
C07	`12`	`X`	`FRE`	`@0 Sélection dirigée @5 37`
C07	`12`	`X`	`ENG`	`@0 Artificial selection @5 37`
C07	`12`	`X`	`SPA`	`@0 Selección dirigida @5 37`
C07	`13`	`X`	`FRE`	`@0 Zootechnie @5 38`
C07	`13`	`X`	`ENG`	`@0 Zootechny @5 38`
C07	`13`	`X`	`SPA`	`@0 Zootecnia @5 38`
C07	`14`	`X`	`FRE`	`@0 Animal laitier @4 INC @5 81`
C07	`15`	`X`	`FRE`	`@0 Bovin @4 INC @5 82`
C07	`16`	`X`	`FRE`	`@0 Herbivore @4 INC @5 83`
C07	`17`	`X`	`FRE`	`@0 Femelle @4 INC @5 84`
C07	`18`	`X`	`FRE`	`@0 Biomathématique @4 INC @5 86`
C07	`19`	`X`	`FRE`	`@0 Sciences animales @4 INC @5 87`
C07	`20`	`X`	`FRE`	`@0 Sélection génétique @4 INC @5 88`
N21				`@1 294`

Format Inist (serveur)

NO :	PASCAL 08-0448742 INIST
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

Links to Exploration step

Pascal:08-0448742

Le document en format XML

<record><TEI><teiHeader><fileDesc><titleStmt><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><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>
</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>
</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><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>
</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>Biological marker</term>
<term>Breeding value</term>
<term>Cow</term>
<term>Dairy cattle</term>
<term>Data analysis</term>
<term>Disease</term>
<term>Health</term>
<term>Hidden Markov model</term>
<term>Mastitis</term>
<term>Mixed model</term>
<term>Morbidity</term>
<term>Prediction</term>
<term>Probability</term>
<term>Rate</term>
<term>Somatic cell</term>
<term>Use</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>
</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>
<inist><standard h6="B"><pA><fA01 i1="01" i2="1"><s0>0999-193X</s0>
</fA01>
<fA03 i2="1"><s0>Genet. sel. evol. : (Print)</s0>
</fA03>
<fA05><s2>40</s2>
</fA05>
<fA06><s2>5</s2>
</fA06>
<fA08 i1="01" i2="1" l="ENG"><s1>The analysis of disease biomarker data using a mixed hidden Markov model (Open Access publication)</s1>
</fA08>
<fA11 i1="01" i2="1"><s1>DETILLEUX (Johann C.)</s1>
</fA11>
<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>
</fA14>
<fA20><s1>491-509</s1>
</fA20>
<fA21><s1>2008</s1>
</fA21>
<fA23 i1="01"><s0>ENG</s0>
</fA23>
<fA43 i1="01"><s1>INIST</s1>
<s2>14418</s2>
<s5>354000197485090020</s5>
</fA43>
<fA44><s0>0000</s0>
<s1>© 2008 INIST-CNRS. All rights reserved.</s1>
</fA44>
<fA45><s0>22 ref.</s0>
</fA45>
<fA47 i1="01" i2="1"><s0>08-0448742</s0>
</fA47>
<fA60><s1>P</s1>
</fA60>
<fA61><s0>A</s0>
</fA61>
<fA64 i1="01" i2="1"><s0>Genetics selection evolution : (Print)</s0>
</fA64>
<fA66 i1="01"><s0>FRA</s0>
</fA66>
<fC01 i1="01" l="ENG"><s0>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.</s0>
</fC01>
<fC02 i1="01" i2="X"><s0>002A07C01B</s0>
</fC02>
<fC02 i1="02" i2="X"><s0>002A36C03</s0>
</fC02>
<fC03 i1="01" i2="X" l="FRE"><s0>Maladie</s0>
<s5>01</s5>
</fC03>
<fC03 i1="01" i2="X" l="ENG"><s0>Disease</s0>
<s5>01</s5>
</fC03>
<fC03 i1="01" i2="X" l="SPA"><s0>Enfermedad</s0>
<s5>01</s5>
</fC03>
<fC03 i1="02" i2="X" l="FRE"><s0>Marqueur biologique</s0>
<s5>02</s5>
</fC03>
<fC03 i1="02" i2="X" l="ENG"><s0>Biological marker</s0>
<s5>02</s5>
</fC03>
<fC03 i1="02" i2="X" l="SPA"><s0>Marcador biológico</s0>
<s5>02</s5>
</fC03>
<fC03 i1="03" i2="X" l="FRE"><s0>Analyse donnée</s0>
<s5>03</s5>
</fC03>
<fC03 i1="03" i2="X" l="ENG"><s0>Data analysis</s0>
<s5>03</s5>
</fC03>
<fC03 i1="03" i2="X" l="SPA"><s0>Análisis datos</s0>
<s5>03</s5>
</fC03>
<fC03 i1="04" i2="X" l="FRE"><s0>Modèle Markov caché</s0>
<s5>04</s5>
</fC03>
<fC03 i1="04" i2="X" l="ENG"><s0>Hidden Markov model</s0>
<s5>04</s5>
</fC03>
<fC03 i1="04" i2="X" l="SPA"><s0>Modelo Markov oculto</s0>
<s5>04</s5>
</fC03>
<fC03 i1="05" i2="X" l="FRE"><s0>Modèle mixte</s0>
<s5>05</s5>
</fC03>
<fC03 i1="05" i2="X" l="ENG"><s0>Mixed model</s0>
<s5>05</s5>
</fC03>
<fC03 i1="05" i2="X" l="SPA"><s0>Modelo mixto</s0>
<s5>05</s5>
</fC03>
<fC03 i1="06" i2="X" l="FRE"><s0>Utilisation</s0>
<s5>06</s5>
</fC03>
<fC03 i1="06" i2="X" l="ENG"><s0>Use</s0>
<s5>06</s5>
</fC03>
<fC03 i1="06" i2="X" l="SPA"><s0>Uso</s0>
<s5>06</s5>
</fC03>
<fC03 i1="07" i2="X" l="FRE"><s0>Bovin laitier</s0>
<s5>07</s5>
</fC03>
<fC03 i1="07" i2="X" l="ENG"><s0>Dairy cattle</s0>
<s5>07</s5>
</fC03>
<fC03 i1="07" i2="X" l="SPA"><s0>Ganado de leche</s0>
<s5>07</s5>
</fC03>
<fC03 i1="08" i2="X" l="FRE"><s0>Vache</s0>
<s5>08</s5>
</fC03>
<fC03 i1="08" i2="X" l="ENG"><s0>Cow</s0>
<s5>08</s5>
</fC03>
<fC03 i1="08" i2="X" l="SPA"><s0>Vaca</s0>
<s5>08</s5>
</fC03>
<fC03 i1="09" i2="X" l="FRE"><s0>Cellule somatique</s0>
<s5>09</s5>
</fC03>
<fC03 i1="09" i2="X" l="ENG"><s0>Somatic cell</s0>
<s5>09</s5>
</fC03>
<fC03 i1="09" i2="X" l="SPA"><s0>Célula somática</s0>
<s5>09</s5>
</fC03>
<fC03 i1="10" i2="X" l="FRE"><s0>Taux</s0>
<s5>10</s5>
</fC03>
<fC03 i1="10" i2="X" l="ENG"><s0>Rate</s0>
<s5>10</s5>
</fC03>
<fC03 i1="10" i2="X" l="SPA"><s0>Tasa</s0>
<s5>10</s5>
</fC03>
<fC03 i1="11" i2="X" l="FRE"><s0>Mastite</s0>
<s5>11</s5>
</fC03>
<fC03 i1="11" i2="X" l="ENG"><s0>Mastitis</s0>
<s5>11</s5>
</fC03>
<fC03 i1="11" i2="X" l="SPA"><s0>Mastitis</s0>
<s5>11</s5>
</fC03>
<fC03 i1="12" i2="X" l="FRE"><s0>Valeur génétique</s0>
<s5>12</s5>
</fC03>
<fC03 i1="12" i2="X" l="ENG"><s0>Breeding value</s0>
<s5>12</s5>
</fC03>
<fC03 i1="12" i2="X" l="SPA"><s0>Valor genético</s0>
<s5>12</s5>
</fC03>
<fC03 i1="13" i2="X" l="FRE"><s0>Prédiction</s0>
<s5>13</s5>
</fC03>
<fC03 i1="13" i2="X" l="ENG"><s0>Prediction</s0>
<s5>13</s5>
</fC03>
<fC03 i1="13" i2="X" l="SPA"><s0>Predicción</s0>
<s5>13</s5>
</fC03>
<fC03 i1="14" i2="X" l="FRE"><s0>Morbidité</s0>
<s5>17</s5>
</fC03>
<fC03 i1="14" i2="X" l="ENG"><s0>Morbidity</s0>
<s5>17</s5>
</fC03>
<fC03 i1="14" i2="X" l="SPA"><s0>Morbilidad</s0>
<s5>17</s5>
</fC03>
<fC03 i1="15" i2="X" l="FRE"><s0>Santé</s0>
<s5>18</s5>
</fC03>
<fC03 i1="15" i2="X" l="ENG"><s0>Health</s0>
<s5>18</s5>
</fC03>
<fC03 i1="15" i2="X" l="SPA"><s0>Salud</s0>
<s5>18</s5>
</fC03>
<fC03 i1="16" i2="X" l="FRE"><s0>Probabilité</s0>
<s5>19</s5>
</fC03>
<fC03 i1="16" i2="X" l="ENG"><s0>Probability</s0>
<s5>19</s5>
</fC03>
<fC03 i1="16" i2="X" l="SPA"><s0>Probabilidad</s0>
<s5>19</s5>
</fC03>
<fC07 i1="01" i2="X" l="FRE"><s0>Artiodactyla</s0>
<s2>NS</s2>
</fC07>
<fC07 i1="01" i2="X" l="ENG"><s0>Artiodactyla</s0>
<s2>NS</s2>
</fC07>
<fC07 i1="01" i2="X" l="SPA"><s0>Artiodactyla</s0>
<s2>NS</s2>
</fC07>
<fC07 i1="02" i2="X" l="FRE"><s0>Ungulata</s0>
<s2>NS</s2>
</fC07>
<fC07 i1="02" i2="X" l="ENG"><s0>Ungulata</s0>
<s2>NS</s2>
</fC07>
<fC07 i1="02" i2="X" l="SPA"><s0>Ungulata</s0>
<s2>NS</s2>
</fC07>
<fC07 i1="03" i2="X" l="FRE"><s0>Mammalia</s0>
<s2>NS</s2>
</fC07>
<fC07 i1="03" i2="X" l="ENG"><s0>Mammalia</s0>
<s2>NS</s2>
</fC07>
<fC07 i1="03" i2="X" l="SPA"><s0>Mammalia</s0>
<s2>NS</s2>
</fC07>
<fC07 i1="04" i2="X" l="FRE"><s0>Vertebrata</s0>
<s2>NS</s2>
</fC07>
<fC07 i1="04" i2="X" l="ENG"><s0>Vertebrata</s0>
<s2>NS</s2>
</fC07>
<fC07 i1="04" i2="X" l="SPA"><s0>Vertebrata</s0>
<s2>NS</s2>
</fC07>
<fC07 i1="05" i2="X" l="FRE"><s0>Boeuf</s0>
</fC07>
<fC07 i1="05" i2="X" l="ENG"><s0>Ox</s0>
</fC07>
<fC07 i1="05" i2="X" l="SPA"><s0>Buey</s0>
</fC07>
<fC07 i1="06" i2="X" l="FRE"><s0>Animal élevage</s0>
<s5>31</s5>
</fC07>
<fC07 i1="06" i2="X" l="ENG"><s0>Farming animal</s0>
<s5>31</s5>
</fC07>
<fC07 i1="06" i2="X" l="SPA"><s0>Animal cría</s0>
<s5>31</s5>
</fC07>
<fC07 i1="07" i2="X" l="FRE"><s0>Animal ruminant</s0>
<s5>32</s5>
</fC07>
<fC07 i1="07" i2="X" l="ENG"><s0>Ruminant animal</s0>
<s5>32</s5>
</fC07>
<fC07 i1="07" i2="X" l="SPA"><s0>Animal rumiante</s0>
<s5>32</s5>
</fC07>
<fC07 i1="08" i2="X" l="FRE"><s0>Pathologie de la glande mammaire</s0>
<s2>NM</s2>
<s5>33</s5>
</fC07>
<fC07 i1="08" i2="X" l="ENG"><s0>Mammary gland diseases</s0>
<s2>NM</s2>
<s5>33</s5>
</fC07>
<fC07 i1="08" i2="X" l="SPA"><s0>Glándula mamaria patología</s0>
<s2>NM</s2>
<s5>33</s5>
</fC07>
<fC07 i1="09" i2="X" l="FRE"><s0>Méthode statistique</s0>
<s5>34</s5>
</fC07>
<fC07 i1="09" i2="X" l="ENG"><s0>Statistical method</s0>
<s5>34</s5>
</fC07>
<fC07 i1="09" i2="X" l="SPA"><s0>Método estadístico</s0>
<s5>34</s5>
</fC07>
<fC07 i1="10" i2="X" l="FRE"><s0>Génétique quantitative</s0>
<s5>35</s5>
</fC07>
<fC07 i1="10" i2="X" l="ENG"><s0>Quantitative genetics</s0>
<s5>35</s5>
</fC07>
<fC07 i1="10" i2="X" l="SPA"><s0>Genética cuantitativa</s0>
<s5>35</s5>
</fC07>
<fC07 i1="11" i2="X" l="FRE"><s0>Médecine vétérinaire</s0>
<s5>36</s5>
</fC07>
<fC07 i1="11" i2="X" l="ENG"><s0>Veterinary medicine</s0>
<s5>36</s5>
</fC07>
<fC07 i1="11" i2="X" l="SPA"><s0>Medicina veterinaria</s0>
<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>
</fC07>
<fC07 i1="12" i2="X" l="SPA"><s0>Selección dirigida</s0>
<s5>37</s5>
</fC07>
<fC07 i1="13" i2="X" l="FRE"><s0>Zootechnie</s0>
<s5>38</s5>
</fC07>
<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>
</fC07>
<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>
<s5>83</s5>
</fC07>
<fC07 i1="17" i2="X" l="FRE"><s0>Femelle</s0>
<s4>INC</s4>
<s5>84</s5>
</fC07>
<fC07 i1="18" i2="X" l="FRE"><s0>Biomathématique</s0>
<s4>INC</s4>
<s5>86</s5>
</fC07>
<fC07 i1="19" i2="X" l="FRE"><s0>Sciences animales</s0>
<s4>INC</s4>
<s5>87</s5>
</fC07>
<fC07 i1="20" i2="X" l="FRE"><s0>Sélection génétique</s0>
<s4>INC</s4>
<s5>88</s5>
</fC07>
<fN21><s1>294</s1>
</fN21>
</pA>
</standard>
<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>
</server>
</inist>
</record>

Pour manipuler ce document sous Unix (Dilib)

EXPLOR_STEP=$WICRI_ROOT/Wicri/Belgique/explor/OpenAccessBelV2/Data/PascalFrancis/Corpus

HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 000075 | SxmlIndent | more

HfdSelect -h $EXPLOR_AREA/Data/PascalFrancis/Corpus/biblio.hfd -nk 000075 | SxmlIndent | more

Pour mettre un lien sur cette page dans le réseau Wicri

{{Explor lien
   |wiki=    Wicri/Belgique
   |area=    OpenAccessBelV2
   |flux=    PascalFrancis
   |étape=   Corpus
   |type=    RBID
   |clé=     Pascal:08-0448742
   |texte=   The analysis of disease biomarker data using a mixed hidden Markov model (Open Access publication)
}}

This area was generated with Dilib version V0.6.25.
Data generation: Thu Dec 1 00:43:49 2016. Site generation: Wed Mar 6 14:51:30 2024

	Serveur d'exploration autour du libre accès en Belgique
	Attention, ce site est en cours de développement ! Attention, site généré par des moyens informatiques à partir de corpus bruts. Les informations ne sont donc pas validées.

Serveur d'exploration autour du libre accès en Belgique

The analysis of disease biomarker data using a mixed hidden Markov model (Open Access publication)

The analysis of disease biomarker data using a mixed hidden Markov model (Open Access publication)

Source :

Descripteurs français

English descriptors

Abstract

Notice en format standard (ISO 2709)

Format Inist (serveur)

Links to Exploration step

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri