---
res:
  bibo_abstract:
  - "This thesis consists of two pieces of work in the broader field of computational
    biology,\r\nboth of which are methods for the analysis of large scale biological
    data, implemented in\r\nefficient software.\r\nChapter 2 introduces a statistical
    software for causal discovery and inference from observed\r\ngenetic marker and
    phenotypic trait data. We explore in simulation how well the method\r\ncan fine-map
    genetic effects, find the correct causal structure among tens of traits and\r\nmillions
    of genetic markers, and infer the causal effect size for the discovered causal\r\nrelations.
    We then apply the method to 8 million markers and 17 traits from the UK\r\nBiobank
    and show that many relationships found with other methods are likely due to\r\nthe
    effects of hidden confounders.\r\nChapter 3 describes how this method can be applied
    to longitudinal data. I show how one\r\ncan incorporate the background knowledge
    present in the known order of measurements to\r\nimprove the accuracy of the causal
    discovery process, and explore the method’s ability to\r\nidentify age specific
    genetic effects, and how the error rates of this recovery are influenced\r\nby
    missing data due to different censoring mechanisms.\r\nChapter 4 introduces a
    statistical software for the comparison of chromatin contact maps\r\nbased on
    the structural similarity index. We explore the robustness of the method to\r\nnoise
    and size differences of the compared maps, show how it can measure evolutionary\r\nconservation
    of topological features by providing a similarity ranking of syntenic regions,\r\nand
    finally how it can detect alterations in 3D genome structure due to genetic mutations\r\nin
    samples of medical relevance.\r\n@eng"
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Nick N
      foaf_name: Machnik, Nick N
      foaf_surname: Machnik
      foaf_workInfoHomepage: http://www.librecat.org/personId=3591A0AA-F248-11E8-B48F-1D18A9856A87
    orcid: 0000-0001-6617-9742
  bibo_doi: 10.15479/at:ista:18642
  dct_date: 2024^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/2663-337X
  dct_language: eng
  dct_publisher: Institute of Science and Technology Austria@
  dct_title: Algorithms for causal learning and comparative analysis for genomic data@
...