[{"citation":{"chicago":"Machnik, Nick N. “Algorithms for Causal Learning and Comparative Analysis for Genomic Data.” Institute of Science and Technology Austria, 2024. <a href=\"https://doi.org/10.15479/at:ista:18642\">https://doi.org/10.15479/at:ista:18642</a>.","ista":"Machnik NN. 2024. Algorithms for causal learning and comparative analysis for genomic data. Institute of Science and Technology Austria.","apa":"Machnik, N. N. (2024). <i>Algorithms for causal learning and comparative analysis for genomic data</i>. Institute of Science and Technology Austria. <a href=\"https://doi.org/10.15479/at:ista:18642\">https://doi.org/10.15479/at:ista:18642</a>","short":"N.N. Machnik, Algorithms for Causal Learning and Comparative Analysis for Genomic Data, Institute of Science and Technology Austria, 2024.","mla":"Machnik, Nick N. <i>Algorithms for Causal Learning and Comparative Analysis for Genomic Data</i>. Institute of Science and Technology Austria, 2024, doi:<a href=\"https://doi.org/10.15479/at:ista:18642\">10.15479/at:ista:18642</a>.","ieee":"N. N. Machnik, “Algorithms for causal learning and comparative analysis for genomic data,” Institute of Science and Technology Austria, 2024.","ama":"Machnik NN. Algorithms for causal learning and comparative analysis for genomic data. 2024. doi:<a href=\"https://doi.org/10.15479/at:ista:18642\">10.15479/at:ista:18642</a>"},"date_published":"2024-12-11T00:00:00Z","researchdata_availability":"unclear","has_accepted_license":"1","file_date_updated":"2025-06-12T22:30:02Z","acknowledgement":"I would like to thank the Swiss National Science Foundation for funding parts of this work\r\nthrough the Eccellenza Grant \"Improving estimation and prediction of common complex\r\ndisease risk\" with grant number PCEGP3_181181.","corr_author":"1","alternative_title":["ISTA Thesis"],"user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","doi":"10.15479/at:ista:18642","day":"11","supervisor":[{"last_name":"Robinson","first_name":"Matthew Richard","orcid":"0000-0001-8982-8813","full_name":"Robinson, Matthew Richard","id":"E5D42276-F5DA-11E9-8E24-6303E6697425"}],"status":"public","ddc":["576"],"month":"12","author":[{"last_name":"Machnik","orcid":"0000-0001-6617-9742","full_name":"Machnik, Nick N","id":"3591A0AA-F248-11E8-B48F-1D18A9856A87","first_name":"Nick N"}],"related_material":{"record":[{"status":"public","relation":"part_of_dissertation","id":"18648"},{"id":"8707","status":"public","relation":"part_of_dissertation"}]},"date_updated":"2026-10-02T11:35:40Z","file":[{"file_name":"NickMachnikThesisFinal_pdfa_conv.pdf","access_level":"open_access","date_updated":"2025-06-12T22:30:02Z","relation":"main_file","checksum":"d45e4d170f9a70a1f69b44b99bd058e4","content_type":"application/pdf","file_id":"18649","creator":"nmachnik","date_created":"2024-12-11T11:59:54Z","file_size":12845009,"embargo":"2025-06-12"},{"embargo_to":"open_access","creator":"nmachnik","content_type":"application/zip","checksum":"f88c9acc62002395ec4dcbdb5eea8b82","file_id":"18650","file_name":"thesis.zip","access_level":"closed","relation":"source_file","date_updated":"2025-06-12T22:30:02Z","file_size":14189810,"date_created":"2024-12-11T11:59:34Z"}],"abstract":[{"text":"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","lang":"eng"}],"page":"138","publisher":"Institute of Science and Technology Austria","type":"dissertation","project":[{"grant_number":"PCEGP3_181181","_id":"9B8D11D6-BA93-11EA-9121-9846C619BF3A","name":"Improving estimation and prediction of common complex disease risk"}],"year":"2024","language":[{"iso":"eng"}],"doi_confirm":"1","supplementarymaterial":"unclear","article_processing_charge":"No","oa":1,"oa_version":"Published Version","fulldoi":"https://doi.org/10.15479/at:ista:18642","publication_status":"published","OA_place":"publisher","publication_identifier":{"issn":["2663-337X"]},"date_created":"2024-12-10T13:49:15Z","department":[{"_id":"GradSch"},{"_id":"MaRo"}],"degree_awarded":"PhD","title":"Algorithms for causal learning and comparative analysis for genomic data","_id":"18642"}]