The major challenge in analysing omic datasets is the strong dependencies which are present between samples and features. Taking into account and modelling the different dependency structures can... Show moreThe major challenge in analysing omic datasets is the strong dependencies which are present between samples and features. Taking into account and modelling the different dependency structures can lead to further improvements of our knowledge of the biological mechanisms. Therefore, improving our ability to predict diseases. This dissertation focuses on the development of new statistical methods designed to take into account the existing structures inside omic datasets by using mixed models, Gaussian graphical models, and machine learning approaches. Show less
Data from high throughput experiments often produce far more results than can ever appear in the main text or tables of a single research article. In these cases, the majority of new associations... Show moreData from high throughput experiments often produce far more results than can ever appear in the main text or tables of a single research article. In these cases, the majority of new associations are often archived either as supplemental information in an arbitrary format or in publisher-independent databases that can be difficult to find. These data are not only lost from scientific discourse, but are also elusive to automated search, retrieval and processing. Here, we use the nanopublication model to make scientific assertions that were concluded from a workflow analysis of Huntington's Disease data machine-readable, interoperable, and citable. We followed the nanopublication guidelines to semantically model our assertions as well as their provenance metadata and authorship. We demonstrate interoperability by linking nanopublication provenance to the Research Object model. These results indicate that nanopublications can provide an incentive for researchers to expose data that is interoperable and machine-readable for future use and preservation for which they can get credits for their effort. Nanopublications can have a leading role into hypotheses generation offering opportunities to produce large-scale data integration. Show less
Mina, E.; Thompson, M.; Kaliyaperumal, R.; Zhao, J.; Horst, E. van der; Tatum, Z.; ... ; Roos, M. 2015
High-grade osteosarcoma is a primary bone tumor with complex genetic alterations, for which targeted therapy is lacking. The aim of this thesis was to use high-throughput molecular data analysis of... Show moreHigh-grade osteosarcoma is a primary bone tumor with complex genetic alterations, for which targeted therapy is lacking. The aim of this thesis was to use high-throughput molecular data analysis of high-grade osteosarcoma specimens and model systems, in order to learn more on osteosarcomagenesis and to find possible ways to inhibit this process. By analyzing different microarray data types using a systems biology approach, genomic instability was identified as an important driver of osteosarcomagenesis. A protective role of macrophages against metastasis of osteosarcoma was detected. In addition, the IR/IGF1R and PI3K/Akt signaling pathways were discovered as potential targets for treatment. This thesis provides the first steps in unraveling the genomic and transcriptomic landscape of high-grade osteosarcoma, and provides a biological rationale for certain new options for adjuvant treatment of this highly genomica lly unstable tumor. Show less
Notwithstanding the hereditary colorectal cancer (CRC) syndromes where the underlying genetic defects have been characterized, for the vast majority of familial cases (15-20%) the disease-causing... Show moreNotwithstanding the hereditary colorectal cancer (CRC) syndromes where the underlying genetic defects have been characterized, for the vast majority of familial cases (15-20%) the disease-causing genes remain unknown, posing serious problems for genetic counselling and patient management of individuals at risk. Aiming to improve the molecular classification of familial CRC, an omics-based approach employing BAC (aCGH), cDNA microarrays and distinct in silico analytical strategies were used to study both the genomic and expression profiles generated from a large collection of colorectal adenomatous polyps. Though the overall results showed a high degree of similarity shared by genomic and expression profiles of familial colonic adenomas, which casts several serious doubts on the classification of hereditary CRC syndromes by omics technologies, the presented findings also argue in favour of a common molecular basis for CRC formation and contribute to the elucidation of the early events associated with colorectal tumorigenesis, namely the early occurrence of CIN and of the complex cross-talk between signalling pathways, in hereditary and in sporadic CRC and even across-species, and additionally in highlighting the power of integrated approaches in the discovery and prioritization of putative targets involved in colorectal tumorigenesis. Show less
