---
res:
  bibo_abstract:
  - "This thesis consists of two pieces of work in the broader feld of computational
    biology,\r\nboth of which are methods for the analysis of large scale biological
    data, implemented in\r\nefcient 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 fne-map
    genetic efects, fnd the correct causal structure among tens of traits and\r\nmillions
    of genetic markers, and infer the causal efect 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
    efects 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 specifc
    genetic efects, and how the error rates of this recovery are infuenced\r\nby missing
    data due to diferent 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
    diferences 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
    fnally 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@
...