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List of FiguresC.1. Time to run fmpr over 50 independent replications. (a) p = 400, K = 10. (b)N = 100, K = 10. (c) N = 100, p = 100. . . . . . . . . . . . . . . . . . . . . . . . . 253xxiv
List of Tables4.1. Clinical and demographic characteristics of the patients in the five breast cancerdatasets. Samples were removed if they were censored before the 5-year cutoffor were treated with adjuvant therapy. The clinical summaries are for thecleaned version of the data (post-removal). Grade: histologic grade. Therapy:neoadjuvant or adjuvant therapy. 1Q: first quartile; Med: median; 3Q: thirdquartile. ER status: estrogen receptor status. . . . . . . . . . . . . . . . . . . . 644.2. The gene set statistics used in this work. . . . . . . . . . . . . . . . . . . . . . . 714.3. Top 10 gene sets by average rank over the five datasets, using the set centroidstatistic. GO enrichment p-values are from a Bonferroni-adjusted one-sidedFisher’s exact test (30,330 tests). Sign=−1 if expression is negatively associatedwith long-term survival, and is +1 otherwise. The background list for the testincludes all Affymetrix HG-U133A probesets that could be mapped to GO BPterms, excluding IEA annotations. . . . . . . . . . . . . . . . . . . . . . . . . . . 804.4. Breakdown of samples for each cancer subtype . . . . . . . . . . . . . . . . . . . 854.5. Top 10 MSigDB sets for ER/HER2 molecular subtypes, chosen by the centroidclassifier using the set centroid statistic. Sign=−1 if expression is negativelyassociated with long-term survival, and +1 for positive association with longtermsurvival. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 904.6. Top 10 sets using the set centroid statistic using different classifiers, and thep-value for the size of the intersection between the top individual genes andthe top gene sets (Fisher’s exact test, one sided). CC is centroid classifier, LRis logistic regression. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 916.1. List of discovery datasets used in this analysis. The 1958 British Birth Cohort(N = 1480) and the National Blood Service (N = 1458) datasets wereused as shared controls for all WTCCC datasets.† Celiac1 used IlluminaHumanHap33v1-1 for cases and HumanHap550-2v3 for controls, and Celiac2-UK used Illumina 670-QuadCustom-v1 for cases and Illumina 1.2M-DuoCustomv1for controls. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128xxv
Page 1: Scalable Approaches for Analysis of
Page 5: DeclarationThis is to certify that1
Page 9 and 10: AcknowledgmentThanks are due to my
Page 11: ContentsList of Abbreviationsxxix1.
Page 14 and 15: Contents8.3. Methods . . . . . . .
Page 16 and 17: List of Figures3.1. An illustration
Page 18 and 19: List of Figures5.2. Left: LOESS-smo
Page 20 and 21: List of Figures7.12. Top 5 principa
Page 22 and 23: List of FiguresB.1. APRC for HAPGEN
Page 26 and 27: List of Tables6.2. List of independ
Page 29: List of AbbreviationsAUCFNFPGEOGiBG
Page 32 and 33: 1. Introductionmolecular marker dat
Page 34 and 35: 1. IntroductionThesis Outline and C
Page 36 and 37: 1. IntroductionChapter 7 — Charac
Page 39 and 40: 2Biological BackgroundIn this chapt
Page 41 and 42: 2.2. The Molecular Basis for Diseas
Page 43 and 44: 2.3. Gene Expression• mRNA extrac
Page 45 and 46: 2.3. Gene Expressionanalyses, and s
Page 47 and 48: 2.3. Gene Expression• Tissue spec
Page 49 and 50: 2.3. Gene ExpressionepigeneticsDNAm
Page 51 and 52: REVIEWS2.4. The Genetic Basis of Di
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Page 55 and 56: 2.4. The Genetic Basis of Diseaseth
Page 57 and 58: TECHNICAL REPORTS2.4. The Genetic B
Page 59 and 60: 2.4. The Genetic Basis of Diseaseno
Page 61 and 62: 2.4. The Genetic Basis of Diseasesa
Page 63 and 64: 2.4. The Genetic Basis of Diseasewi
Page 65 and 66: 3Review of the Analysis of Gene Exp
Page 67 and 68: 3.2. Supervised Machine LearningEmp
Page 69 and 70: 3.3. Linear Models and Loss Functio
Page 71 and 72: 3.4. Feature Selection — Finding
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3.4. Feature Selection — Finding
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3. Review of the Analysis of Gene E
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4. Gene Sets for Breast Cancer Prog
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5Fast and Memory-Efficient Sparse L
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5.2. BackgroundThe lasso can be for
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5.3. Design Considerations5.3. Desi
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5.4. MethodsThere are two key aspec
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5.4. Methodssome stage during the a
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5.4. Methodsset convergence means t
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5.4. Methods5.4.6. Discrimination a
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5.5. Resultsdiscovery and validatio
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5.5. Results5.5.1. SparSNP makes po
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5.6. Software Featuresresults avera
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5.7. Discussion5.7. DiscussionWe ha
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6Sparse Linear Models Explain Pheno
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6.2. MethodsLasso ModelsWe used l 1
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6.2. Methods6.2.2. HAPGEN2 simulati
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6.3. Results6.2.5. Data and quality
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6.3. Results1005000.80.60.40.22500+
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6.3. ResultsDisease Abbrev. Cases C
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6.3. Results0.9AUC0.80.70.6DatasetB
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6.3. Results1.00.8PPV0.60.4●●
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6.3. ResultsT1D models trained on t
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6.4. Discussionconfidence — cases
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6.5. ConclusionsFrom a computationa
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7. Genetic Control of the Human Met
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8Fused Multitask Penalised Regressi
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8.2. BackgroundRecently, Kim and Xi
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8.3. Methodsβ−0.20 −0.10 0.00
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8.4. Simulationwhere L is the loss
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8.4. Simulation• The fused ridge
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8.4. Simulation30Same sparsity, sam
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8.5. Resultslikely due to random va
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●●●●●●●Lasso●●Rid
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8. Fused Multitask Penalised Regres
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9. ConclusionsThis assumption is ne
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9. Conclusionsof results. There is
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9. Conclusionsin general, since the
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A. Supplementary Results for Gene S
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BSupplementary Results for Sparse L
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B.2. Real Dataare highly unlikely
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B.2. Real Data• differential miss
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B.2. Real Datafor Celiac1/Celiac2-U
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B.2. Real Data1005000.80.60.40.2250
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B.2. Real Data(a) Original Celiac1
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B. Supplementary Results for Sparse
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BibliographyK. L. Ayers and H. J. C
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BibliographyH. Brentani, O. L. Caba
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BibliographyH. Dai, L. van’t Veer
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BibliographyC. M. van Duijn, Y. S.
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BibliographyV. Guerrero. Time-serie
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BibliographyA. V. Ivshina, J. Georg
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BibliographyT. A. Mckinsey, K. Kuwa
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BibliographyL. Pusztai, C. Mazouni,
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BibliographyD. Botstein, P. E. Løn
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BibliographyP. J. van Diest, E. van
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BibliographyZ. Wei and H. Li. Nonpa
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