Search for dissertations about: "Jan Komorowski"

Showing result 11 - 15 of 20 swedish dissertations containing the words Jan Komorowski.

  2. 11. Patterns in big data bioinformatics : Understanding complex diseases with interpretable machine learning

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

    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

  4. 12. Understanding Complex Diseases and Disease Causative Agents : The Machine Learning way

    Author : Zeeshan Khaliq; Jan Komorowski; Steven Bosinger; Uppsala universitet; []
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; Pathogens; Influenza A viruses; Human immunodeficiency virus; Simian immunodeficiency virus; Pathogenicity; Cancer; long noncoding RNAs; Machine learning; Host specificity; Host-specific signatures; Bioinformatics; Bioinformatik;

    Abstract : Diseases can be caused by foreign agents – pathogens – such as viruses, bacteria and other parasites, entering the body or by an internal malfunction of the body itself. The partial understanding of diseases like cancer and the ones caused by viruses, like the influenza A viruses (IAVs) and the human immunodeficiency virus, means we still do not have an efficient cure or defence against them. READ MORE

  5. 13. From Physicochemical Features to Interdependency Networks : A Monte Carlo Approach to Modeling HIV-1 Resistome and Post-translational Modifications

    Author : Marcin Kierczak; Jan Komorowski; Anna Tramontano; Uppsala universitet; []
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; bioinformatics; HIV-1; resistome analysis; drug resistance; predicting PTMs; molecular interdependency networks; MCFS-ID; feature selection; interactome; machine-learning; rough sets; Bioinformatics; Bioinformatik; Computer Science; datavetenskap; Biology; with specialization in structural biology; biologi; med inriktning mot strukturbiologi;

    Abstract : The availability of new technologies supplied life scientists with large amounts of experimental data. The data sets are large not only in terms of the number of observations, but also in terms of the number of recorded features. READ MORE

  6. 14. Modeling the Interaction Space of Biological Macromolecules: A Proteochemometric Approach : Applications for Drug Discovery and Development

    Author : Aleksejs Kontijevskis; Jarl Wikberg; Jan Komorowski; Robert Glen; Uppsala universitet; []
    Keywords : Bioinformatics; proteochemometrics; bioinformatics; chemoinformatics; chemical space; QSAR; retroviral proteases; HIV-1; drug resistance; pharmacogenomics; cytochrome P450; GPCRs; melanocortin receptors; interactome; machine-learning; rough sets; Bioinformatik;

    Abstract : Molecular interactions lie at the heart of myriad biological processes. Knowledge of molecular recognition processes and the ability to model and predict interactions of any biological molecule to any chemical compound are the key for better understanding of cell functions and discovery of more efficacious medicines. READ MORE

  7. 15. Rule-Based Approaches for Large Biological Datasets Analysis : A Suite of Tools and Methods

    Author : Marcin Kruczyk; Jan Komorowski; Joaquin Dopazo; []
    Keywords : MEDICIN OCH HÄLSOVETENSKAP; MEDICAL AND HEALTH SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; Rough sets; peak finding; gliomas; Alzheimer disease; STAT3; machine learning; feature selection; next generation sequencing;

    Abstract : This thesis is about new and improved computational methods to analyze complex biological data produced by advanced biotechnologies. Such data is not only very large but it also is characterized by very high numbers of features. READ MORE