The biological clock regulates daily and seasonal rhythms in mammals. This clock is located in the suprachiasmatic nuclei (SCN), which are two small nuclei each consisting of 10,000 neurons. The... Show moreThe biological clock regulates daily and seasonal rhythms in mammals. This clock is located in the suprachiasmatic nuclei (SCN), which are two small nuclei each consisting of 10,000 neurons. The neurons of the SCN endogenously generate a rhythm of approximately 24 hours. Under the influence of the light-dark cycle, the SCN produce a coordinated output that is subjected to daily environmental changes. The adaptation to the light-dark cycle is a property of the neuronal network of the SCN. This neuronal network also explains the adjustment to long summer days and short winter days, and to shifts in the light-dark cycle caused by transatlantic flights or shift work. In this thesis the neuronal network of the SCN is investigated using computational techniques. The computer simulations were directed by experimental results, while, vice versa, new experiments were guided by results from the simulations. These coordinated efforts of computational science and life sciences show how properties emerge at the neuronal network level, that are not present in individual cells. Show less
The increase in capabilities of information technology of the last decade has led to a large increase in the creation of raw data. Data mining, a form of computer guided, statistical data analysis,... Show moreThe increase in capabilities of information technology of the last decade has led to a large increase in the creation of raw data. Data mining, a form of computer guided, statistical data analysis, attempts to draw knowledge from these sources that is usable, human understandable and was previously unknown. One of the potential application domains is that of law enforcement. This thesis describes a number of efforts in this direction and reports on the results reached on the application of its resulting algorithms on actual police data. The usage of specifically tailored data mining algorithms is shown to have a great potential in this area, which forebodes a future where algorithmic assistance in "combating" crime will be a valuable asset. Show less
Bio-informatica kan omschreven worden als het toepassen van algoritmen om meerwaarde te verkrijgen uit data afkomstig van biomedisch en/of biologisch onderzoek. In bio-informatica wordt onderzoek... Show moreBio-informatica kan omschreven worden als het toepassen van algoritmen om meerwaarde te verkrijgen uit data afkomstig van biomedisch en/of biologisch onderzoek. In bio-informatica wordt onderzoek gedaan met grote gegevens verzamelingen die afkomstig zijn uit biomedisch en/of biologisch experimenten. Het doel van dit onderzoek is komen tot nieuwe inzichten vanuit de gegevens verzameling. Deze inzichten komen tot stand door de goede organisatie van de data, het linken naar en integreren met complementaire gegevens verzamelingen en ontwikkelen en toepassen van analytische methodieken. Als bio-informatica groep onderzoeken wij het inrichten en ontwikkelen van een 3D spatio-temporele data omgeving voor ontwikkelingsstudies van het zebravis model organisme. De expressie van genen in spatio-temporale patronen vormt de basis van het ontwikkelingsproces. Voor onderzoekers is een begrip van deze patronen in sam enhang met de anatomische ontwikkeling belangrijk; hoe vormen de patronen de basis voor vorm verandering en welke genen kunnen bij dergelijke veranderende patronen betrokken zijn. In deze context hebben wij een omgeving ontwikkeld voor spatio-temporele gegevens uit embryonische studies van het zebravis modelsysteem. Show less
This dissertation introduces new design methodology for automated design, programming, and implementation of multiprocessor systems-on-chip (MPSoCs) starting at a high level of abstraction. The... Show moreThis dissertation introduces new design methodology for automated design, programming, and implementation of multiprocessor systems-on-chip (MPSoCs) starting at a high level of abstraction. The proposed methodology offers a fully integrated tool-flow for very fast exploration and implementation of alternative MPSoCs, where design space exploration, system-level synthesis, application mapping, and system prototyping of MPSoCs are highly automated. The main idea is starting from a functional specification of an application and a description of an MPSoC at system level, to refine and translate them to lower register transfer level (RTL) descriptions in a systematic and automated way. This is achieved by applying a model-driven approach which exploits the platform-based design concept. In particular, to model an application, we use the Kahn Process Network (KPN) model of computation, which has proved to be well suited for specifying streaming applications in a parallel form. In addition, we have devised a platform model which allows for building MPSoCs in a systematic and automated way that execute KPNs efficiently. We use a mapping model to express the relation between the processes and the communication channels in the application (KPN) and the processing and memory components of the platform. Show less
A data mining scenario is a logical sequence of steps to infer patterns from data. In this thesis, we present two scenarios. Our first scenario aims to identify homogeneous subtypes in data. It was... Show moreA data mining scenario is a logical sequence of steps to infer patterns from data. In this thesis, we present two scenarios. Our first scenario aims to identify homogeneous subtypes in data. It was applied to clinical research on Osteoarthritis (OA) and Parkinson’s disease (PD) and in drug discovery. Thus, because OA and PD are characterized by clinical heterogeneity, a more sensitive classification of the cohort of patients may contribute to the search for the underlying diseases mechanism. In drug discovery, subtyping may improve the understanding of the similarity (and distance) between different phenotypic effects as induced by drugs and chemicals. Our second scenario aims to compare text classification algorithms. First, we show that common classifiers achieve comparable performance on most problems. Second, tightly constrained SVM solutions are high performers. In that situation, most training documents are bounded support vectors, SVM reduces to a nearest mean classifier and no training is necessary, which raises a question on SVM merits in sparse bag of words feature spaces. Also, SVM is shown to suffer from performance deterioration for particular combinations of training set size/number of features. This relate to outlying documents of distinct classes overlapping in the feature space. Show less
