Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 16 of 16 for “"Gene selection"”.
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Gene Selection and Cancer Classification Using a Multidimensional Fuzzy Deep Learning Approach for Gene Expression Data
… cancer prediction models using associated gene expression and mutation data. This thesis provides a comprehensive review of recent cancer studies that have employed gene expression data from several cancer types (i.e Breast, Lung, Kidney, Liver, Gallbladder, Gastric, and Thyroid) for …
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Machine Learning to Interrogate High-throughput Genomic Data: Theory and Applications
… The interaction effects among risk factors, both genetic and environmental, are hypothesized to be one of the main missing heritability sources. Moreover, detection of multilocus interaction effect may also have great implications for revealing disease/biological mechanisms, for accurate risk …
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Large scale disease prediction
… possibility of combining a large amount of heterogeneous data to perform gene selection and phenotype classification. First, a subset of publicly available microarray datasets was downloaded from the NCBI Gene Expression Omnibus (GEO) [18, 5]. This data was then automatically tagged with Unified …
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Discovery of multi-omics biomarkers for toxicity using meta-analysis
… and defined toxicity level 17 2. Biomarker selection and predictive model generation 18 2-1. Gene selection using meta-analysis 18 2-2. Functional analysis of filtered genes by meta-analysis 19 2-3. Gene selection using sPLS-DA approach 19 2-4. Gene selection using wrappers 19 2-5. …
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Finding Informative Genes in Subtypes of Breast Cancer
… ten subtypes of breast cancer. Unlike existing gene selection approaches, we use a hierarchical based classification approach that selects genes and builds the classifier concurrently in a top-down fashion. We also propose a new bottom-up hierarchical approach to obtain the most informative …
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Prediction of CYP3A4 metabolic activity from whole genome RNA-seq data with feature selection machine learning methods
… project are to identify hepatically-expressed genes that are associated with CYP3A4 metabolic activity in human liver tissue and to predict CYP3A4 activity using gene expression data from whole genome RNA sequences. Due to the high-dimensionality of the dataset, we applied lasso and elastic …
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Contribution to Statistical Techniques for Identifying Differentially Expressed Genes in Microarray Data
… measure the expression levels of thousands of genes (features or genomic biomarkers) simultaneously in one single experiment. Robust and accurate gene selection methods are required to identify differentially expressed genes across different samples for disease diagnosis or prognosis. The …
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Unveiling Phenotype–Genotype Interplay with Deep Learning Foundation Models for scRNA-seq: A Quantitative Perspective
… large-scale pretraining to capture complex gene expression patterns. However, a comprehensive quantitative framework for understanding the interplay between phenotypes and genotypes remains underdeveloped. Such a framework is critical not only for validating model performance but also for …
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Expression and regulation of genes associated with CNS regeneration and plasticity
… 1 B. Factors for the failure of CNS axon regeneration 2 C. Conditioning injury (CI)-induced axon regeneration 3 D. Regeneration associated genes (RAGs) 4 E. Epigenetics 5 F. Neuroepigenetics in CNS injury 8 G. Next generation sequencing (NGS) in epigenomic studies 10 H. Aims of this study 11 …
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Improving Statistical Learning within Functional Genomic Experiments by means of Feature Selection
… technology allow measuring tens of thousands of genes (features) simultaneously. However, the expressions of these genes are usually observed in a small number, tens to few hundreds, of tissue samples (observations). This common characteristic of high dimensionality has a great impact on the …
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Application of Informatics Tools to Facilitate the Practice of Precision Medicine with Genomic Testing and Clinical Data
… tools to visualize data pertaining to the gene selection practices of pharmacogenomic (PGx) tests effectively communicated large amounts of information into concise heatmaps. After a thorough search identifying potential PGx tests, their detection rates were assessed based on their gene …
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Feature selection for cancer classification using microarray gene expression data
… to measure the expression levels of thousands of genes simultaneously and allows biologists easily gain insight into the complex interaction in tumours on gene expression levels. Its application in cancer studies has been shown great success in both diagnosis and elucidating the pathological …
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A study on endocrine disrupters in the environment through the microarray technology
… present in the promoter region of the target gene, very well described for many target genes, but that also other mechanisms are used: the interaction of the ER with the AP 1, Sp 1 and NFkB modes, that are discovered but not yet comprehensively described. The aim of my work is to produce a …
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Ensemble Tree-Based Machine Learning for Imaging Data
… processes. The detailed information generated by medical imaging has enabled physicians to provide more comprehensive care. Although numerous machine learning algorithms, especially those used for imaging data, have been developed, dealing with unique structures in imaging data …
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Parameter reduction in deep learning and classification
… to over-fit the training data, and hence not generalize well, while in deep learning, neural networks have shown to achieve state-of-the-art results, especially in the area of image recognition, in their current state cannot be easily deployed on memory restricted Internet-of-Things devices. …