| 1. |
- Andersson, Robin, et al.
(författare)
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RoSy : A Rough Knowledge Base System
- 2005
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Ingår i: Rough Sets, Fuzzy Sets, Data Mining, and Granular-Soft Computing,2005. - Berlin : Springer. ; , s. 48-
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Konferensbidrag (refereegranskat)
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| 2. |
- Barrenäs, Fredrik, 1981-
(författare)
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Bioinformatic identification of disease associated pathways by network based analysis
- 2012
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Many common diseases are complex, meaning that they are caused by many interacting genes. This makes them difficult to study; to determine disease mechanisms, disease-associated genes must be analyzed in combination. Disease-associated genes can be detected using high-throughput methods, such as mRNA expression microarrays, DNA methylation microarrays and genome-wide association studies (GWAS), but determining how they interact to cause disease is an intricate challenge. One approach is to organize disease-associated genes into networks using protein-protein interactions (PPIs) and dissect them to identify disease causing pathways. Studies of complex disease can also be greatly facilitated by using an appropriate model system. In this dissertation, seasonal allergic rhinitis (SAR) served as a model disease. SAR is a common disease that is relatively easy to study. Also, the key disease cell types, like the CD4+ T cell, are known and can be cultured and activated in vitro by the disease causing pollen.The aim of this dissertation was to determine network properties of disease-associated genes, and develop methods to identify and validate networks of disease-associated genes. First, we showed that disease-associated genes have distinguishing network properties, one being that they co-localize in the human PPI network. This supported the existence of disease modules within the PPI network. We then identified network modules of genes whose mRNA expression was perturbed in human disease, and showed that the most central genes in those network modules were enriched for disease-associated polymorphisms identified by GWAS. As a case study, we identified disease modules using mRNA expression data from allergen-challenged CD4+ cells from patients with SAR. The case study identified and validated a novel disease-associated gene, FGF2 using GWAS data and RNAi mediated knockdown.Lastly, we examined how DNA methylation caused disease-associated mRNA expression changes in SAR. DNA methylation, but not mRNA expression profiles, could accurately distinguish allergic patients from healthy controls. Also, we found that disease-associated mRNA expression changes were associated with a low DNA methylation content and absence of CpG islands. Specifically within this group, we found a correlation between disease-associated mRNA expression changes and DNA methylation changes. Using ChIP-chip analysis, we found that targets of a known disease relevant transcription factor, IRF4, were also enriched among non CpG island genes with low methylation levels.Taken together, in this dissertation the network properties of disease-associated genes were examined, and then used to validate disease networks defined by mRNA expression data. We then examined regulatory mechanisms underlying disease-associated mRNA expression changes in a model disease. These studies support network-based analyses as a method to understand disease mechanisms and identify important disease causing genes, such as treatment targets or markers for personalized medication.
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| 3. |
- Komorowski, Henryk Jan, 1952-
(författare)
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A specification of an abstract Prolog machine and its application to partial evaluation
- 1981
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- We investigate partial evalution of Prolog programs as a part of a theory of interactive, incremental programming. The goal of of this investigation is to provide formally correct, interactive programming tools for program transformation.An abstract Prolog machine is introduced. The machine is systematically extended to an abstract partial evaluation Prolog machine. Three fundamental partial evaluation transformations are introduced and proved to preserve meaning of programs: pruning, forward data structure propagation, and opening (which also provides backward data structure propagation). The theoretical investigation is then extended to account for relations between logic and partial evalution.An implementation of a partial evalution system is then developed from the formal specification. The system is well integrated and efficiently implemented in the Qlog programming environment. Several examples illustrate the mechanism and applications of partial evalution.Finally, we outline how meta-rules that control the execution of the Prolog program can be incorporated into the system in a clean way. Such rules are familiar from artifical intelligence research. They could be used in future programming environments as specialized metatheories which support the programmer in particular tasks of programming.
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| 4. |
- Magnusson, Rasmus, 1992-
(författare)
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High Confidence Network Predictions from Big Biological Data
- 2020
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Biology functions in a most intriguing fashion, with human cells being regulated by multiplex networks of proteins and their dependent systems that control everything from proliferation to cell death. Notably, there are cases when these networks fail to function properly. In some diseases there are multiple small perturbations that push the otherwise healthy cells into a state of malfunction. These maladies are referred to as complex diseases, and include common disorders such as allergy, diabetes type II, and multiple sclerosis, and due to their complexity there is no universally defined approach to fully understand their pathogenesis or pathophysiology. While these perturbations can be measured using high-throughput technologies, the interplay of these perturbations is generally to complex to understand without any structured mathematical analysis. There is today numerous such methods that put the small perturbations of complex diseases into relation of interactions among each other. However, the methods have historically struggled with notable uncertainty in their predictions.This uncertainty can be addressed by at least two different approaches. First, mechanistically realistic mathematical modelling is an approach that has the capacity to accurately describe almost any biological system, but such models can to-date only describe small systems and networks. Secondly, large-scale mathematical modelling approaches exist, but the faithfulness of the models to the underlying biology has been compromised to achieve algorithms that are computationally effective.In this Ph.D. thesis, I suggest how high confidence predictions of network interactions can be extracted from big biological. First, I show how large-scale data can be used when building high-quality ODE models (Paper I). Secondly, by developing the software LASSIM, I show how ODE models can be expanded to the size of entire cell systems (Paper II). However, while LASSIM showed that powerful non-linear ODE-modelling can be applied to understand big biological data, it still remained a machine learning-based approach in contrast to hypothesis-driven model development.Instead, two more studies revolving around large-scale modelling approaches were initiated. The third study suggested that ambiguities in model selection and interaction identification greatly compromise the accuracy of available tools, and that the novel software of Paper III, LiPLike, can be used to remove such predictions. Intriguingly, while LiPLike was able to effectively discard false identifications, the accuracy of predictions remained relatively low. This low accuracy was thought to arise from model simplifications, and therefore the next study aimed at finding methods that come closer to the true biological system (Paper IV). In particular, the study aimed at predicting protein abundance -the true mediators of biological functionality- from the much more easily accessible mRNA levels, and found that such models could be used to get several new insights on protein mechanisms, which was exemplified by the identification of important biomarkers of autoimmune diseases.The analysis of big biological data and the underlying networks is a centrepiece of understanding both diseases and how cell functionality is orchestrated. The work that is presented in this Ph.D. thesis represents a journey between fields with different views on how these networks should be inferred. In particular, it aimed to combine the accuracy of small-scale mechanistic modelling with the system-spanning potential of large-scale linear system modelling, and this thesis thus provides a tool-bench of methods and insights on how knowledge can be extracted from big biological data, and in extension it is a small step towards a generation of new comprehensions of biological systems and complex diseases.
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