Dissertation/ Thesis
Histogram based Hierarchical Data Representation for Microarray Classification ; Histograma basado en Representación jerárquica para datos de Microarray Clasificación ; Histograma basat en Representació jeràrquica per dades de Microarray Classificació
| Τίτλος: | Histogram based Hierarchical Data Representation for Microarray Classification ; Histograma basado en Representación jerárquica para datos de Microarray Clasificación ; Histograma basat en Representació jeràrquica per dades de Microarray Classificació |
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| Συγγραφείς: | Kottath, Sandeep |
| Συνεισφορές: | Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions, Salembier Clairon, Philippe Jean |
| Στοιχεία εκδότη: | Universitat Politècnica de Catalunya |
| Έτος έκδοσης: | 2012 |
| Συλλογή: | Universitat Politècnica de Catalunya, BarcelonaTech: UPCommons - Global access to UPC knowledge |
| Θεματικοί όροι: | Àrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Bioinformàtica, Àrees temàtiques de la UPC::Enginyeria biomèdica::Electrònica biomèdica, Bioengineering, Proximity detectors, Genomes --Data processing, DNA microarrays--Data processing, metagenes, dominant genes, hierarchical clustering, EMD, feature selection, LDA (Linear Discriminant Analysis), KNN (K-Nearest Neighbour), wrapper, Bioenginyeria, Detectors de proximitat, Genomes, Imatges -- Compressió (Informàtica), Imatges -- Processament, Intel·ligència artificial, Microxips d'ADN |
| Περιγραφή: | [ANGLÈS] A general framework for microarray classification relying on histogram based hierarchical clustering is proposed in this work. It produces precise and reliable classifiers based on a two-step approach. In the first step, the feature set is enhanced by histogram based features corresponding to each cluster produced via hierarchical clustering, where a parameter (maximum number of dominant genes) can be tuned based on the dataset characteristics. In the second step, a reliable classifier is built from a wrapper feature selection process called Improved Sequential Floating Forward Selection (IFFS) to properly choose a small feature set for the classification task. Considering the sample scarcity in the microarray datasets, a reliability parameter has been considered to improve the feature selection process along with classification error rate. Different combinations of error rate and reliability has been used as the scoring rule. Linear Discriminant Analysis (LDA) and K-Nearest Neighbour (KNN) classifiers have been used for this work and the performances has been compared. The potential of the proposed framework has been evaluated with three publicly available datasets : colon, lymphoma and leukaemia. The experimental results have confirmed the usefulness of the histogram based hierarchical clustering and the new representative feature generation algorithm. A gene level analysis has revealed that the best features selected by the feature selection algorithm has only very few basic constituent genes involved. The comparative results showed that the proposed framework can compete with state of the art alternatives. ; [CASTELLÀ] Un marco general para la clasificación de microarrays se propone en este trabajo. Produce clasificadores precisos y fiables basados en un enfoque de dos pasos. En el primer paso, el conjunto de características se ve reforzado por una serie de características basado en un histograma correspondiente a cada racimo producido a través de la agrupación jerárquica, donde puede ser un ... |
| Τύπος εγγράφου: | master thesis |
| Περιγραφή αρχείου: | application/pdf |
| Γλώσσα: | English |
| Relation: | https://hdl.handle.net/2099.1/16018 |
| Διαθεσιμότητα: | https://hdl.handle.net/2099.1/16018 |
| Rights: | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ ; Open Access ; Attribution-NonCommercial-NoDerivs 3.0 Spain |
| Αριθμός Καταχώρησης: | edsbas.D98E4F07 |
| Βάση Δεδομένων: | BASE |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://hdl.handle.net/2099.1/16018# Name: EDS - BASE (ns324271) Category: fullText Text: View record from BASE |
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| Header | DbId: edsbas DbLabel: BASE An: edsbas.D98E4F07 RelevancyScore: 766 AccessLevel: 3 PubType: Dissertation/ Thesis PubTypeId: dissertation PreciseRelevancyScore: 765.984252929688 |
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| Items | – Name: Title Label: Title Group: Ti Data: Histogram based Hierarchical Data Representation for Microarray Classification ; Histograma basado en Representación jerárquica para datos de Microarray Clasificación ; Histograma basat en Representació jeràrquica per dades de Microarray Classificació – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Kottath%2C+Sandeep%22">Kottath, Sandeep</searchLink> – Name: Author Label: Contributors Group: Au Data: Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions<br />Salembier Clairon, Philippe Jean – Name: Publisher Label: Publisher Information Group: PubInfo Data: Universitat Politècnica de Catalunya – Name: DatePubCY Label: Publication Year Group: Date Data: 2012 – Name: Subset Label: Collection Group: HoldingsInfo Data: Universitat Politècnica de Catalunya, BarcelonaTech: UPCommons - Global access to UPC knowledge – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Àrees+temàtiques+de+la+UPC%3A%3AInformàtica%3A%3AAplicacions+de+la+informàtica%3A%3ABioinformàtica%22">Àrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Bioinformàtica</searchLink><br /><searchLink fieldCode="DE" term="%22Àrees+temàtiques+de+la+UPC%3A%3AEnginyeria+biomèdica%3A%3AElectrònica+biomèdica%22">Àrees temàtiques de la UPC::Enginyeria biomèdica::Electrònica biomèdica</searchLink><br /><searchLink fieldCode="DE" term="%22Bioengineering%22">Bioengineering</searchLink><br /><searchLink fieldCode="DE" term="%22Proximity+detectors%22">Proximity detectors</searchLink><br /><searchLink fieldCode="DE" term="%22Genomes+--Data+processing%22">Genomes --Data processing</searchLink><br /><searchLink fieldCode="DE" term="%22DNA+microarrays--Data+processing%22">DNA microarrays--Data processing</searchLink><br /><searchLink fieldCode="DE" term="%22metagenes%22">metagenes</searchLink><br /><searchLink fieldCode="DE" term="%22dominant+genes%22">dominant genes</searchLink><br /><searchLink fieldCode="DE" term="%22hierarchical+clustering%22">hierarchical clustering</searchLink><br /><searchLink fieldCode="DE" term="%22EMD%22">EMD</searchLink><br /><searchLink fieldCode="DE" term="%22feature+selection%22">feature selection</searchLink><br /><searchLink fieldCode="DE" term="%22LDA+%28Linear+Discriminant+Analysis%29%22">LDA (Linear Discriminant Analysis)</searchLink><br /><searchLink fieldCode="DE" term="%22KNN+%28K-Nearest+Neighbour%29%22">KNN (K-Nearest Neighbour)</searchLink><br /><searchLink fieldCode="DE" term="%22wrapper%22">wrapper</searchLink><br /><searchLink fieldCode="DE" term="%22Bioenginyeria%22">Bioenginyeria</searchLink><br /><searchLink fieldCode="DE" term="%22Detectors+de+proximitat%22">Detectors de proximitat</searchLink><br /><searchLink fieldCode="DE" term="%22Genomes%22">Genomes</searchLink><br /><searchLink fieldCode="DE" term="%22Imatges+--+Compressió+%28Informàtica%29%22">Imatges -- Compressió (Informàtica)</searchLink><br /><searchLink fieldCode="DE" term="%22Imatges+--+Processament%22">Imatges -- Processament</searchLink><br /><searchLink fieldCode="DE" term="%22Intel·ligència+artificial%22">Intel·ligència artificial</searchLink><br /><searchLink fieldCode="DE" term="%22Microxips+d'ADN%22">Microxips d'ADN</searchLink> – Name: Abstract Label: Description Group: Ab Data: [ANGLÈS] A general framework for microarray classification relying on histogram based hierarchical clustering is proposed in this work. It produces precise and reliable classifiers based on a two-step approach. In the first step, the feature set is enhanced by histogram based features corresponding to each cluster produced via hierarchical clustering, where a parameter (maximum number of dominant genes) can be tuned based on the dataset characteristics. In the second step, a reliable classifier is built from a wrapper feature selection process called Improved Sequential Floating Forward Selection (IFFS) to properly choose a small feature set for the classification task. Considering the sample scarcity in the microarray datasets, a reliability parameter has been considered to improve the feature selection process along with classification error rate. Different combinations of error rate and reliability has been used as the scoring rule. Linear Discriminant Analysis (LDA) and K-Nearest Neighbour (KNN) classifiers have been used for this work and the performances has been compared. The potential of the proposed framework has been evaluated with three publicly available datasets : colon, lymphoma and leukaemia. The experimental results have confirmed the usefulness of the histogram based hierarchical clustering and the new representative feature generation algorithm. A gene level analysis has revealed that the best features selected by the feature selection algorithm has only very few basic constituent genes involved. The comparative results showed that the proposed framework can compete with state of the art alternatives. ; [CASTELLÀ] Un marco general para la clasificación de microarrays se propone en este trabajo. Produce clasificadores precisos y fiables basados en un enfoque de dos pasos. En el primer paso, el conjunto de características se ve reforzado por una serie de características basado en un histograma correspondiente a cada racimo producido a través de la agrupación jerárquica, donde puede ser un ... – Name: TypeDocument Label: Document Type Group: TypDoc Data: master thesis – Name: Format Label: File Description Group: SrcInfo Data: application/pdf – Name: Language Label: Language Group: Lang Data: English – Name: NoteTitleSource Label: Relation Group: SrcInfo Data: https://hdl.handle.net/2099.1/16018 – Name: URL Label: Availability Group: URL Data: https://hdl.handle.net/2099.1/16018 – Name: Copyright Label: Rights Group: Cpyrght Data: http://creativecommons.org/licenses/by-nc-nd/3.0/es/ ; Open Access ; Attribution-NonCommercial-NoDerivs 3.0 Spain – Name: AN Label: Accession Number Group: ID Data: edsbas.D98E4F07 |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.D98E4F07 |
| RecordInfo | BibRecord: BibEntity: Languages: – Text: English Subjects: – SubjectFull: Àrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Bioinformàtica Type: general – SubjectFull: Àrees temàtiques de la UPC::Enginyeria biomèdica::Electrònica biomèdica Type: general – SubjectFull: Bioengineering Type: general – SubjectFull: Proximity detectors Type: general – SubjectFull: Genomes --Data processing Type: general – SubjectFull: DNA microarrays--Data processing Type: general – SubjectFull: metagenes Type: general – SubjectFull: dominant genes Type: general – SubjectFull: hierarchical clustering Type: general – SubjectFull: EMD Type: general – SubjectFull: feature selection Type: general – SubjectFull: LDA (Linear Discriminant Analysis) Type: general – SubjectFull: KNN (K-Nearest Neighbour) Type: general – SubjectFull: wrapper Type: general – SubjectFull: Bioenginyeria Type: general – SubjectFull: Detectors de proximitat Type: general – SubjectFull: Genomes Type: general – SubjectFull: Imatges -- Compressió (Informàtica) Type: general – SubjectFull: Imatges -- Processament Type: general – SubjectFull: Intel·ligència artificial Type: general – SubjectFull: Microxips d'ADN Type: general Titles: – TitleFull: Histogram based Hierarchical Data Representation for Microarray Classification ; Histograma basado en Representación jerárquica para datos de Microarray Clasificación ; Histograma basat en Representació jeràrquica per dades de Microarray Classificació Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Kottath, Sandeep – PersonEntity: Name: NameFull: Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions – PersonEntity: Name: NameFull: Salembier Clairon, Philippe Jean IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2012 Identifiers: – Type: issn-locals Value: edsbas – Type: issn-locals Value: edsbas.oa |
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