Sökning: "expression data"

Visar resultat 1 - 5 av 2034 avhandlingar innehållade orden expression data.

  1. 1. Statistical analysis of gene expression data

    Författare :Erik Kristiansson; Göteborgs universitet; []
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; gene expression; DNA microarrays; linear models; empirical Bayes; quality control; gene regulation; categorical data analysis; logistic regression; heavy metal stress; ecotoxicology; gene expression;

    Sammanfattning : 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. LÄS MER

  3. 2. Integrating multi-omics for type 2 diabetes : Data science and big data towards personalized medicine

    Författare :Klev Diamanti; Jan Komorowski; Claes Wadelius; Manfred Grabherr; Peter Spégel; Uppsala universitet; []
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; MEDICIN OCH HÄLSOVETENSKAP; MEDICAL AND HEALTH SCIENCES; type 2 diabetes; multi-omics; genomics; metabolomics; data science; machine learning; personalized medicine; Bioinformatics; Bioinformatik;

    Sammanfattning : 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. LÄS MER

  4. 3. Uncovering biomarkers and molecular heterogeneity of complex diseases : Utilizing the power of Data Science

    Författare :Sara Younes; Linda Holmfeldt; Jan Komoroski; Aedin Culhane; Uppsala universitet; []
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; MEDICIN OCH HÄLSOVETENSKAP; MEDICAL AND HEALTH SCIENCES; MEDICIN OCH HÄLSOVETENSKAP; MEDICAL AND HEALTH SCIENCES; Complex Disease; Cancer; Autoimmune diseases; Acute Myeloid Leukemia; Systemic Lupus Erythematosus; Bioinformatics; Machine Learning; Data Science; Statistical Analysis; Bioinformatics; Bioinformatik; Computer Science; Datavetenskap;

    Sammanfattning : 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. LÄS MER

  5. 4. Patterns in big data bioinformatics : Understanding complex diseases with interpretable machine learning

    Författare :Mateusz Garbulowski; Jan Komorowski; Ryan J. Urbanowicz; Uppsala universitet; []
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; complex diseases; big data; machine learning; transcriptomics; life sciences; rough sets; Bioinformatics; Bioinformatik;

    Sammanfattning : 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. LÄS MER

  7. 5. Vector Representations of Idioms in Data-Driven Chatbots for Robust Assistance

    Författare :Oluwatosin Adewumi; Marcus Liwicki; Foteini Liwicki; Taiwo Kolajo; Luleå tekniska universitet; []
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; conversational system; chatbot; vectors; data; deep models; Maskininlärning; Machine Learning;

    Sammanfattning : 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. LÄS MER