Transcriptional Characterization of Glioma Neural Stem Cells Diva ...

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8.5 Target Prediction Ensemble Analysis Results Figure 8.4: Step by step diagram of the ensemble method adopted to find the score E (=C2/C1) of prediction accuracy for prediction algorithms. The method was applied to the predictions from all combinations of target prediction algorithms and an E-score was generated for each one. The red set of gene predictions varied in size depending on how many and which algorithms were being considered for a particular round. predicting than the single, we performed the same steps 1-7 but with lists that resulted as the union of all the user-defined prediction algorithm genes (vary- ing size of the predicted genes set of figure 8.4). The results are listed in table 8.5. The purpose of the hit list is to "weight" the importance to the predicted genes. For example, if gene A has been predicted to be targeted by only nine of the 258 microRNAs, its weight when added to the cumulative score C1 will be only 9/258th the weight added by gene B, if gene B were predicted to be 214
8.5 Target Prediction Ensemble Analysis Results Table 8.2: Single prediction algorithm ensemble analysis results. Displayed in descending order of E-score. Prediction name E-score ElMMo 0,3181 Diana-microT 0,3122 PITA 0,3088 TargetscanS 0,3073 PicTar 6 0,3020 miRBase 0,3008 PicTar 5 0,2989 miRanda 0,2979 targeted by all 258 microRNAs. This scoring system takes into consideration the possibility that not all prediction algorithms necessarily predict the target- ing for all the 258 up-regulated microRNAs, by always measuring the accuracy of a prediction set over the number of genes of the background gene list that are predicted by a particular algorithm. This analysis highlights the fact that very little improvement, in the order of the thousandth, is achieved by combining prediction algorithms together. The highest scoring prediction algorithms as-singles are ElMMo and Diana-microT and when used in combination with other prediction algorithms the E-score of ElMMo always decreases, while the E-score of Diana-microT, in combination with one or two other prediction algorithms, seems to slightly increase. This analysis reveals that the best performing combination of algorithms does not outperform the best scoring as-single algorithm ElMMo. However, this initial analysis should be followed by a series of other analysis in which other approaches for the evaluation of the score are taken, and the Minimum and Cumulative filters are taken into consideration as well. This approach would increase the number of combinations from the current 256 (2 8 ) because each combination of filters would have to be evaluated for all the 256 microRNAs and would therefore require a global energy combinatorial optimisation algorithm such as a genetic algorithm, whereby a search that mimics the process of natural evolution would be performed on a population of candidate solu- tions that evolve towards the best solution starting from a randomly selected population. 215
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Transcriptional Characterization of
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This dissertation is my own work an
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Acknowledgements I would like to th
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5.9 External Dataset Expression Cor
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Introduction 1
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1.1 Glial Cells in the Central Nerv
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1.2 Glioblastoma Multiforme Introdu
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1.3 Primary and Secondary Glioblast
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1.4 Pathways Involved in Glioblasto
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1.5 Pathway Crosstalk Introduction
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Chapter 2 Neurogenesis Contents 2.1
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2.1 Radial Glia Introduction VZ adj
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2.2 Neural Stem Cells Introduction
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Chapter 3 Brain Cancer Stem Cells C
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3.1 The Cancer Stem Cell Hypothesis
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3.1 The Cancer Stem Cell Hypothesis
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3.2 Brain Cancer Stem Cells Introdu
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3.3 Glioma Culture Systems Introduc
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Chapter 4 The Non-Coding RNA World
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4.1 MicroRNA regulation Introductio
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4.1 MicroRNA regulation Introductio
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4.2 Target Prediction and Validatio
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Methods 93
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5.1 Tag-sequencing Data Processing
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5.1 Tag-sequencing Data Processing
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5.2 Array Comparative Genomic Hybri
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5.4 Quantitative Real Time-PCR Vali
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5.5 Literature Mining Methods Figur
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5.5 Literature Mining Methods How m
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5.6 Differential Isoform Expression
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5.9 External Dataset Expression Cor
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5.10 Glioblastoma Pathway Construct
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5.11 MicroRNA Target Prediction Ana
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Results 119
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6.2 Tag mapping Results Table 6.1:
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6.2 Tag mapping Results 123 Figure
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6.3 Copy Number Aberrations Results
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6.3 Copy Number Aberrations Results
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6.4 Core Differentially Expressed G
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6.5 Large-scale qRT-PCR Validation
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6.6 Literature Mining for Different
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6.7 Isoform Differential Expression
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6.8 Long ncRNA Differential Express
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6.8 Long ncRNA Differential Express
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Chapter 7 Dataset Correlation Analy
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A.4 Tag-seq vs qRT-PCR Correlation
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Appendix "BEX5", "DIAPH2", "GBP3",
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End Select End If End Sub Appendix
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If NameStr.ToLower().Equals("query"
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Appendix C Long ncRNAs We detected
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C. Long ncRNAs Appendix Tag Accessi
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D.1 Pathway Interactions Appendix F
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D.2 Pathway Images Appendix Figure
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D.2 Pathway Images Appendix Figure
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E. Exon Array Data Appendix Average
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F. MicroRNA Array Data Appendix Tab
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F. MicroRNA Array Data Appendix GNS
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F. MicroRNA Array Data Appendix GNS
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Ln Natural logarithm LOH Loss of He
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3.3 Schematisation of cell cycle ph
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8.3 Workflows at the core of the pr
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7.1 Selected Gene Ontology terms an
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