Search for dissertations about: "Omics data"

Showing result 1 - 5 of 79 swedish dissertations containing the words Omics data.

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

   Author : Klev Diamanti; Jan Komorowski; Claes Wadelius; Manfred Grabherr; Peter Spégel; Uppsala universitet; []
   Keywords : 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;

   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

4. 2. Image-based multi-omics data integration : Exploring whole-body PET/MRI, -omics data and body composition

   Author : Robin Visvanathar; Håkan Ahlström; Joel Kullberg; Jan Eriksson; Katrine Riklund; Uppsala universitet; []
   Keywords : MEDICIN OCH HÄLSOVETENSKAP; MEDICAL AND HEALTH SCIENCES; Body composition; Imiomics; genomics; metabolomics; MRI; PET; insulin sensitivity; T2D;

   Abstract : Advanced body composition analysis with whole-body imaging could uncover novel associations between regional tissue composition and metabolic disease. Imiomics is an automated image analysis framework that enables large-scale integration of magnetic resonance imaging (MRI) data and orthogonal technologies such as metabolomics and genomics for the detailed study of body composition. READ MORE

5. 3. Interpretation of variation in omics data : Applications in proteomics for sustainable agriculture

   Author : Jakob Willforss; Institutionen för immunteknologi; []
   Keywords : NATURVETENSKAP; NATURAL SCIENCES; agriculture; proteomics; omics; biomarker; normalization; batch effect; visualization; software;

   Abstract : Biomarkers are used in molecular biology to predict characteristics of interest and are applied in agriculture to accelerate the breeding of target traits. Proteomics has emerged as a promising technology for improved markers by providing a closer view to the phenotype than conventional genome-based approaches. READ MORE

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

   Author : Sara Younes; Linda Holmfeldt; Jan Komoroski; Aedin Culhane; Uppsala universitet; []
   Keywords : 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;

   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

7. 5. Omics Data Analysis of Complex Diseases and Traits

   Author : Saman Hosseini Ashtiani; Arne Elofsson; Dirk Repsilber; Stockholms universitet; []
   Keywords : NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; Omics data; RNA-seq; Microarrays; Metabolomics; Network analysis; Psoriasis; E. coli; Hip osteoporosis; Lung adenocarcinoma; Spearman s correlation coefficient; LC-MS; PCA; DEG; PPI; biokemi med inriktning mot bioinformatik; Biochemistry towards Bioinformatics;

   Abstract : Following the advent of the high-throughput techniques for producing massive omics data, new possibilities and challenges have also emerged in different fields of biology and medicine. Dealing with such data on different scales with different scopes such as genomics, transcriptomics, proteomics and metabolomics, demands appropriate data collection, preprocessing, statistical analysis, interpretation and visualization. READ MORE