Dissertation/ Thesis
Identification and assessment of gene signatures in human breast cancer ; Identification et évaluation de signatures géniques dans le cancer du sein humain
| Title: | Identification and assessment of gene signatures in human breast cancer ; Identification et évaluation de signatures géniques dans le cancer du sein humain |
|---|---|
| Authors: | Haibe-Kains, Benjamin |
| Contributors: | Bontempi, Gianluca, Sotiriou, Christos, Lenaerts, Tom, Moreau, Yves, Delorenzi, Mauro, Bersini, Hugues, van Helden, Jacques, Decaestecker, Christine |
| Publisher Information: | Universite Libre de Bruxelles Université libre de Bruxelles, Faculté des Sciences – Informatique, Bruxelles |
| Publication Year: | 2009 |
| Collection: | DI-fusion : dépôt institutionnel de l'Université libre de Bruxelles (ULB) |
| Subject Terms: | Informatique générale, Sciences exactes et naturelles, Breast -- Cancer -- Data processing, Breast -- Cancer -- Genetic aspects -- Data processing, Breast -- Cancer -- Prognosis -- Data processing, DNA microarrays, Gene expression -- Data processing, Sein -- Cancer -- Informatique, Sein -- Cancer -- Aspect génétique -- Informatique, Sein -- Cancer -- Pronostic -- Informatique, Puces à ADN, Expression génique -- Informatique, apprentissage automatique, machine learning |
| Description: | This thesis addresses the use of machine learning techniques to develop clinical diagnostic tools for breast cancer using molecular data. These tools are designed to assist physicians in their evaluation of the clinical outcome of breast cancer (referred to as prognosis). The traditional approach to evaluating breast cancer prognosis is based on the assessment of clinico-pathologic factors known to be associated with breast cancer survival. These factors are used to make recommendations about whether further treatment is required after the removal of a tumor by surgery. Treatment such as chemotherapy depends on the estimation of patients' risk of relapse. Although current approaches do provide good prognostic assessment of breast cancer survival, clinicians are aware that there is still room for improvement in the accuracy of their prognostic estimations. In the late nineties, new high throughput technologies such as the gene expression profiling through microarray technology emerged. Microarrays allowed scientists to analyze for the first time the expression of the whole human genome ("transcriptome"). It was hoped that the analysis of genome-wide molecular data would bring new insights into the critical, underlying biological mechanisms involved in breast cancer progression, as well as significantly improve prognostic prediction. However, the analysis of microarray data is a difficult task due to their intrinsic characteristics: (i) thousands of gene expressions are measured for only few samples; (ii) the measurements are usually "noisy"; and (iii) they are highly correlated due to gene co-expressions. Since traditional statistical methods were not adapted to these settings, machine learning methods were picked up as good candidates to overcome these difficulties. However, applying machine learning methods for microarray analysis involves numerous steps, and the results are prone to overfitting. Several authors have highlighted the major pitfalls of this process in the early publications, shedding new light on ... |
| Document Type: | doctoral or postdoctoral thesis |
| File Description: | 1 full-text file(s): application/pdf |
| Language: | French |
| Relation: | local/ulbcat.ulb.ac.be:843350 |
| Availability: | https://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210348 https://dipot.ulb.ac.be/dspace/bitstream/2013/210348/1/0edb320b-c02a-49c0-bb87-8165a5d231ec.txt |
| Accession Number: | edsbas.83F071EE |
| Database: | BASE |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210348# Name: EDS - BASE (ns324271) Category: fullText Text: View record from BASE |
|---|---|
| Header | DbId: edsbas DbLabel: BASE An: edsbas.83F071EE RelevancyScore: 762 AccessLevel: 3 PubType: Dissertation/ Thesis PubTypeId: dissertation PreciseRelevancyScore: 761.994323730469 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Identification and assessment of gene signatures in human breast cancer ; Identification et évaluation de signatures géniques dans le cancer du sein humain – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Haibe-Kains%2C+Benjamin%22">Haibe-Kains, Benjamin</searchLink> – Name: Author Label: Contributors Group: Au Data: Bontempi, Gianluca<br />Sotiriou, Christos<br />Lenaerts, Tom<br />Moreau, Yves<br />Delorenzi, Mauro<br />Bersini, Hugues<br />van Helden, Jacques<br />Decaestecker, Christine – Name: Publisher Label: Publisher Information Group: PubInfo Data: Universite Libre de Bruxelles<br />Université libre de Bruxelles, Faculté des Sciences – Informatique, Bruxelles – Name: DatePubCY Label: Publication Year Group: Date Data: 2009 – Name: Subset Label: Collection Group: HoldingsInfo Data: DI-fusion : dépôt institutionnel de l'Université libre de Bruxelles (ULB) – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Informatique+générale%22">Informatique générale</searchLink><br /><searchLink fieldCode="DE" term="%22Sciences+exactes+et+naturelles%22">Sciences exactes et naturelles</searchLink><br /><searchLink fieldCode="DE" term="%22Breast+--+Cancer+--+Data+processing%22">Breast -- Cancer -- Data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Breast+--+Cancer+--+Genetic+aspects+--+Data+processing%22">Breast -- Cancer -- Genetic aspects -- Data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Breast+--+Cancer+--+Prognosis+--+Data+processing%22">Breast -- Cancer -- Prognosis -- Data processing</searchLink><br /><searchLink fieldCode="DE" term="%22DNA+microarrays%22">DNA microarrays</searchLink><br /><searchLink fieldCode="DE" term="%22Gene+expression+--+Data+processing%22">Gene expression -- Data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Sein+--+Cancer+--+Informatique%22">Sein -- Cancer -- Informatique</searchLink><br /><searchLink fieldCode="DE" term="%22Sein+--+Cancer+--+Aspect+génétique+--+Informatique%22">Sein -- Cancer -- Aspect génétique -- Informatique</searchLink><br /><searchLink fieldCode="DE" term="%22Sein+--+Cancer+--+Pronostic+--+Informatique%22">Sein -- Cancer -- Pronostic -- Informatique</searchLink><br /><searchLink fieldCode="DE" term="%22Puces+à+ADN%22">Puces à ADN</searchLink><br /><searchLink fieldCode="DE" term="%22Expression+génique+--+Informatique%22">Expression génique -- Informatique</searchLink><br /><searchLink fieldCode="DE" term="%22apprentissage+automatique%22">apprentissage automatique</searchLink><br /><searchLink fieldCode="DE" term="%22machine+learning%22">machine learning</searchLink> – Name: Abstract Label: Description Group: Ab Data: This thesis addresses the use of machine learning techniques to develop clinical diagnostic tools for breast cancer using molecular data. These tools are designed to assist physicians in their evaluation of the clinical outcome of breast cancer (referred to as prognosis). The traditional approach to evaluating breast cancer prognosis is based on the assessment of clinico-pathologic factors known to be associated with breast cancer survival. These factors are used to make recommendations about whether further treatment is required after the removal of a tumor by surgery. Treatment such as chemotherapy depends on the estimation of patients' risk of relapse. Although current approaches do provide good prognostic assessment of breast cancer survival, clinicians are aware that there is still room for improvement in the accuracy of their prognostic estimations. In the late nineties, new high throughput technologies such as the gene expression profiling through microarray technology emerged. Microarrays allowed scientists to analyze for the first time the expression of the whole human genome ("transcriptome"). It was hoped that the analysis of genome-wide molecular data would bring new insights into the critical, underlying biological mechanisms involved in breast cancer progression, as well as significantly improve prognostic prediction. However, the analysis of microarray data is a difficult task due to their intrinsic characteristics: (i) thousands of gene expressions are measured for only few samples; (ii) the measurements are usually "noisy"; and (iii) they are highly correlated due to gene co-expressions. Since traditional statistical methods were not adapted to these settings, machine learning methods were picked up as good candidates to overcome these difficulties. However, applying machine learning methods for microarray analysis involves numerous steps, and the results are prone to overfitting. Several authors have highlighted the major pitfalls of this process in the early publications, shedding new light on ... – Name: TypeDocument Label: Document Type Group: TypDoc Data: doctoral or postdoctoral thesis – Name: Format Label: File Description Group: SrcInfo Data: 1 full-text file(s): application/pdf – Name: Language Label: Language Group: Lang Data: French – Name: NoteTitleSource Label: Relation Group: SrcInfo Data: local/ulbcat.ulb.ac.be:843350 – Name: URL Label: Availability Group: URL Data: https://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210348<br />https://dipot.ulb.ac.be/dspace/bitstream/2013/210348/1/0edb320b-c02a-49c0-bb87-8165a5d231ec.txt – Name: AN Label: Accession Number Group: ID Data: edsbas.83F071EE |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.83F071EE |
| RecordInfo | BibRecord: BibEntity: Languages: – Text: French Subjects: – SubjectFull: Informatique générale Type: general – SubjectFull: Sciences exactes et naturelles Type: general – SubjectFull: Breast -- Cancer -- Data processing Type: general – SubjectFull: Breast -- Cancer -- Genetic aspects -- Data processing Type: general – SubjectFull: Breast -- Cancer -- Prognosis -- Data processing Type: general – SubjectFull: DNA microarrays Type: general – SubjectFull: Gene expression -- Data processing Type: general – SubjectFull: Sein -- Cancer -- Informatique Type: general – SubjectFull: Sein -- Cancer -- Aspect génétique -- Informatique Type: general – SubjectFull: Sein -- Cancer -- Pronostic -- Informatique Type: general – SubjectFull: Puces à ADN Type: general – SubjectFull: Expression génique -- Informatique Type: general – SubjectFull: apprentissage automatique Type: general – SubjectFull: machine learning Type: general Titles: – TitleFull: Identification and assessment of gene signatures in human breast cancer ; Identification et évaluation de signatures géniques dans le cancer du sein humain Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Haibe-Kains, Benjamin – PersonEntity: Name: NameFull: Bontempi, Gianluca – PersonEntity: Name: NameFull: Sotiriou, Christos – PersonEntity: Name: NameFull: Lenaerts, Tom – PersonEntity: Name: NameFull: Moreau, Yves – PersonEntity: Name: NameFull: Delorenzi, Mauro – PersonEntity: Name: NameFull: Bersini, Hugues – PersonEntity: Name: NameFull: van Helden, Jacques – PersonEntity: Name: NameFull: Decaestecker, Christine IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2009 Identifiers: – Type: issn-locals Value: edsbas – Type: issn-locals Value: edsbas.oa |
| ResultId | 1 |