Predicting the prognosis of breast cancer by integrating clinical and microarray data with Bayesian networks
Identifieur interne : 001629 ( Main/Exploration ); précédent : 001628; suivant : 001630Predicting the prognosis of breast cancer by integrating clinical and microarray data with Bayesian networks
Auteurs : Olivier Gevaert [Belgique] ; Frank De Smet [Belgique] ; Dirk Timmerman [Belgique] ; Yves Moreau [Belgique] ; Bart De Moor [Belgique]Source :
- Bioinformatics [ 1367-4803 ] ; 2006-07-15.
Abstract
Motivation: Clinical data, such as patient history, laboratory analysis, ultrasound parameters—which are the basis of day-to-day clinical decision support—are often underused to guide the clinical management of cancer in the presence of microarray data. We propose a strategy based on Bayesian networks to treat clinical and microarray data on an equal footing. The main advantage of this probabilistic model is that it allows to integrate these data sources in several ways and that it allows to investigate and understand the model structure and parameters. Furthermore using the concept of a Markov Blanket we can identify all the variables that shield off the class variable from the influence of the remaining network. Therefore Bayesian networks automatically perform feature selection by identifying the (in)dependency relationships with the class variable. Results: We evaluated three methods for integrating clinical and microarray data: decision integration, partial integration and full integration and used them to classify publicly available data on breast cancer patients into a poor and a good prognosis group. The partial integration method is most promising and has an independent test set area under the ROC curve of 0.845. After choosing an operating point the classification performance is better than frequently used indices. Contact:olivier.gevaert@esat.kuleuven.be
Url:
DOI: 10.1093/bioinformatics/btl230
Affiliations:
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Yves" uniqKey="Moreau Y" first="Yves" last="Moreau">Yves Moreau</name><affiliation wicri:level="3"><country xml:lang="fr">Belgique</country><wicri:regionArea>Department of Electrical Engineering ESAT-SCD, Katholieke Universiteit LeuvenKasteelpark Arenberg 10, 3001 Leuven</wicri:regionArea><placeName><region type="province" nuts="2">Province du Brabant flamand</region><settlement type="town">Heverlee</settlement><settlement type="city">Louvain</settlement></placeName></affiliation></author><author><name sortKey="Moor, Bart De" sort="Moor, Bart De" uniqKey="Moor B" first="Bart De" last="Moor">Bart De Moor</name><affiliation wicri:level="3"><country xml:lang="fr">Belgique</country><wicri:regionArea>Department of Electrical Engineering ESAT-SCD, Katholieke Universiteit LeuvenKasteelpark Arenberg 10, 3001 Leuven</wicri:regionArea><placeName><region type="province" nuts="2">Province du Brabant flamand</region><settlement type="town">Heverlee</settlement><settlement type="city">Louvain</settlement></placeName></affiliation></author></analytic><monogr></monogr><series><title level="j">Bioinformatics</title><idno type="ISSN">1367-4803</idno><idno type="eISSN">1460-2059</idno><imprint><publisher>Oxford University Press</publisher><date type="published" when="2006-07-15">2006-07-15</date><biblScope unit="volume">22</biblScope><biblScope unit="issue">14</biblScope><biblScope unit="page" from="184">e184</biblScope><biblScope unit="page" to="190">e190</biblScope></imprint><idno type="ISSN">1367-4803</idno></series><idno type="istex">76E1D9FCFC5C2E3AB70E00D9D2115D8585842EDB</idno><idno type="DOI">10.1093/bioinformatics/btl230</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">1367-4803</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Motivation: Clinical data, such as patient history, laboratory analysis, ultrasound parameters—which are the basis of day-to-day clinical decision support—are often underused to guide the clinical management of cancer in the presence of microarray data. We propose a strategy based on Bayesian networks to treat clinical and microarray data on an equal footing. The main advantage of this probabilistic model is that it allows to integrate these data sources in several ways and that it allows to investigate and understand the model structure and parameters. Furthermore using the concept of a Markov Blanket we can identify all the variables that shield off the class variable from the influence of the remaining network. Therefore Bayesian networks automatically perform feature selection by identifying the (in)dependency relationships with the class variable. Results: We evaluated three methods for integrating clinical and microarray data: decision integration, partial integration and full integration and used them to classify publicly available data on breast cancer patients into a poor and a good prognosis group. The partial integration method is most promising and has an independent test set area under the ROC curve of 0.845. After choosing an operating point the classification performance is better than frequently used indices. Contact:olivier.gevaert@esat.kuleuven.be</div></front></TEI><affiliations><list><country><li>Belgique</li></country><region><li>Province du Brabant flamand</li><li>Vienne (Autriche)</li></region><settlement><li>Heverlee</li><li>Louvain</li><li>Vienne (Autriche)</li></settlement></list><tree><country name="Belgique"><region name="Province du Brabant flamand"><name sortKey="Gevaert, Olivier" sort="Gevaert, Olivier" uniqKey="Gevaert O" first="Olivier" last="Gevaert">Olivier Gevaert</name></region><name sortKey="Moor, Bart De" sort="Moor, Bart De" uniqKey="Moor B" first="Bart De" last="Moor">Bart De Moor</name><name sortKey="Moreau, Yves" sort="Moreau, Yves" uniqKey="Moreau Y" first="Yves" last="Moreau">Yves Moreau</name><name sortKey="Smet, Frank De" sort="Smet, Frank De" uniqKey="Smet F" first="Frank De" last="Smet">Frank De Smet</name><name sortKey="Smet, Frank De" sort="Smet, Frank De" uniqKey="Smet F" first="Frank De" last="Smet">Frank De Smet</name><name sortKey="Timmerman, Dirk" sort="Timmerman, Dirk" uniqKey="Timmerman D" first="Dirk" last="Timmerman">Dirk Timmerman</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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