Search for dissertations about: "expression data"
Showing result 1 - 5 of 1973 swedish dissertations containing the words expression data.
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1. Statistical analysis of gene expression data
Abstract : Microarray technology has become one of the most important tools for genome-wide mRNA measurements. The technique has been successfully applied to many areas in modern biology including cancer research, identification of drug targets, and categorization of genes involved in the cell cycle. READ MORE
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2. Integrating multi-omics for type 2 diabetes : Data science and big data towards personalized medicine
Abstract : Type 2 diabetes (T2D) is a complex metabolic disease characterized by multi-tissue insulin resistance and failure of the pancreatic β-cells to secrete sufficient amounts of insulin. Cells recruit transcription factors (TF) to specific genomic loci to regulate gene expression that consequently affects the protein and metabolite abundancies. READ MORE
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3. Uncovering biomarkers and molecular heterogeneity of complex diseases : Utilizing the power of Data Science
Abstract : Uncovering causal drivers of complex diseases is yet a difficult challenge. Unlike single-gene disorders complex diseases are heterogeneous and are caused by a combination of genetic, environmental, and lifestyle factors which complicates the identification of patient subgroups and the disease causal drivers. READ MORE
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4. Patterns in big data bioinformatics : Understanding complex diseases with interpretable machine learning
Abstract : Alterations in the flow of genetic information may lead to complex diseases. Such changes are measured with various omics techniques that usually produce the so-called “big data”. Using interpretable machine learning (ML), we retrieved patterns from transcriptomics data sets. READ MORE
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5. Vector Representations of Idioms in Data-Driven Chatbots for Robust Assistance
Abstract : This thesis presents resources capable of enhancing solutions of some Natural Language Processing (NLP) tasks, demonstrates the learning of abstractions by deep models through cross-lingual transferability, and shows how deep learning models trained on idioms can enhance open-domain conversational systems. The challenges of open-domain conversational systems are many and include bland repetitive utterances, lack of utterance diversity, lack of training data for low-resource languages, shallow world-knowledge and non-empathetic responses, among others. READ MORE