Dissertation/ Thesis
Identification and assessment of gene signatures in human breast cancer ; Identification et évaluation de signatures géniques dans le cancer du sein humain
| Τίτλος: | Identification and assessment of gene signatures in human breast cancer ; Identification et évaluation de signatures géniques dans le cancer du sein humain |
|---|---|
| Συγγραφείς: | Haibe-Kains, Benjamin |
| Συνεισφορές: | Bontempi, Gianluca, Sotiriou, Christos, Lenaerts, Tom, Moreau, Yves, Delorenzi, Mauro, Bersini, Hugues, van Helden, Jacques, Decaestecker, Christine |
| Στοιχεία εκδότη: | Universite Libre de Bruxelles Université libre de Bruxelles, Faculté des Sciences – Informatique, Bruxelles |
| Έτος έκδοσης: | 2009 |
| Συλλογή: | DI-fusion : dépôt institutionnel de l'Université libre de Bruxelles (ULB) |
| Θεματικοί όροι: | 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 |
| Περιγραφή: | 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 ... |
| Τύπος εγγράφου: | doctoral or postdoctoral thesis |
| Περιγραφή αρχείου: | 1 full-text file(s): application/pdf |
| Γλώσσα: | French |
| Relation: | local/ulbcat.ulb.ac.be:843350 |
| Διαθεσιμότητα: | 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 |
| Αριθμός Καταχώρησης: | edsbas.83F071EE |
| Βάση Δεδομένων: | BASE |
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