This thesis focuses on data found in the field of computational drug discovery. New insight can be obtained by applying machine learning in various ways and in a variety of domains. Two studies... Show moreThis thesis focuses on data found in the field of computational drug discovery. New insight can be obtained by applying machine learning in various ways and in a variety of domains. Two studies delved into the application of proteochemometrics (PCM), a machine learning technique that can be used to find relations in protein-ligand bioactivity data and then predict using a virtual screen whether compounds that had never been tested on a particular protein, or set of proteins. With this, sets of compounds were suggested for experimental validation that were significant in a myriad of ways. Another study investigated the mutational patterns in cancer, applying a large dataset of mutation data and identifying several motifs in G protein-coupled receptors. The thesis also contains the work done on the Papyrus dataset, a large scale bioactivity dataset that focuses on standardising data for computational drug discovery and providing an out-of-the-box set that can be used in a variety of settings. Show less
Towards a data-driven military. A multi-disciplinary perspective assesses the use of data and information on modern conflict from different scientific and methodological disciplines, aiming to... Show moreTowards a data-driven military. A multi-disciplinary perspective assesses the use of data and information on modern conflict from different scientific and methodological disciplines, aiming to generate valuable contributions to the ongoing discourse on data, the military and modern warfare. Military Systems and Technology approaches the theme empirically by researching how data can enhance the utility of military materiel and subsequently accelerate the decision-making process. War Studies take a multidisciplinary approach to the evolution of warfare, while Military Management Studies take a holistic organisational and procedural approach. Based on their scientific protocols and research methods, the three domains put forward different research questions and perspectives, providing the unique character of this book. Show less
Als gevolg van de grote technologische vooruitgang in de gezondheidszorg worden in toenemende mate gegevens verzameld tijdens de uitvoering van klinische onderzoeken. Het is evenwel essentieel om... Show moreAls gevolg van de grote technologische vooruitgang in de gezondheidszorg worden in toenemende mate gegevens verzameld tijdens de uitvoering van klinische onderzoeken. Het is evenwel essentieel om te beseffen dat gegevens op zich van weinig of geen waarde zijn. Ten behoeve van hun optimale bruikbaarheid dienen gegevens geanalyseerd, geïnterpreteerd en verwerkt te worden. Machine learning-strategieën kunnen hiertoe nuttige en adequate oplossingen bieden. Dit proefschrift bevat machine learning-benaderingen toegepast op verschillende klinische datasets. De klassieke gegevens bestaan uit elektrische signalen van het electrocardiogram (ecg) verkregen bijgezonde proefpersonen, de innovatieve gegevens zijn afkomstig vanmetingen in een rijsimulator, en de opkomende gegevens zijn afgeleid van dna-analyse van de micro-organismen die op de huid voorkomenvan patiënten met huidziekten. We toonden aan dat het aantal ECG’s van invloed was op de nauwkeurigheid van geschatte verlenging van het qt-interval voor alle ingezette qt-correctieformules. Met behulp van SHapley AdditiveexPlanations (shap)-waarden werd de impact van de individuele kenmerken op de voorspelling van fysiologische leeftijd van het hart bepaald. We maakten gebruik van machine learning voor een betere beoordeling van de rijprestaties van bestuurders die medicijnen gebruikten. Tot slot lieten we zien dat de belangrijkste micro-organismen voor discriminatie van seborrroische dermatitis – naast Cutibacterium en Staphylococcus – kwamen relatief weinig voor, waardoor men deze micro-organismen in standaardanalyses eenvoudig over het hoofd kan zien. Daarmee hebben we aangetoond dat machine learning kanworden toegepast op gegevens die zijn afgeleid van klinische onderzoeken om in een vroeg stadium het effect van medicijnen en andere interventies op te sporen en te evalueren. Show less
We live in the digital era, and archaeologists are increasingly engaging with 'big' data. A recent project at Oxford sought new answers about the landscapes of early medieval England from the... Show moreWe live in the digital era, and archaeologists are increasingly engaging with 'big' data. A recent project at Oxford sought new answers about the landscapes of early medieval England from the longue durée. Show less
This article addresses data protection including the automatic processing of personal and business data as a result of the flows of information and the digital trade. Nowadays, data is being... Show moreThis article addresses data protection including the automatic processing of personal and business data as a result of the flows of information and the digital trade. Nowadays, data is being collected, exchanged and used in small or large amounts by governments, international organizations and companies for medical, educational, social, industrial and tax purposes amongst others. The increasingly collection, exchange and use of data by companies and governments, calls for attention to the legal protection in the collection, exchange, use, monitoring and processing of this data. Furthermore, the use of big data also raises questions regarding the protection of privacy and also the safeguards in place for the data controllers among others. The main question of this article is are the instruments in the era of digital trade, internet governance and taxation sufficient to guarantee the privacy and data protection of individuals and business? In order to answer this question, this article will address the challenges and the instruments for the protection of the use of data and big data in three areas: trade and internet governance and taxation. Show less
Brandsen, A.; Lambers, K.; Verberne, S.; Wansleeben, M. 2019
In this paper, we present the results of user requirement solicitation for a search system of grey literature in archaeology, specifically Dutch excavation reports. This search system uses Named... Show moreIn this paper, we present the results of user requirement solicitation for a search system of grey literature in archaeology, specifically Dutch excavation reports. This search system uses Named Entity Recognition and Information Retrieval techniques to create an effective and effortless search experience. Specifically, we used Conditional Random Fields to identify entities, with an average accuracy of 56%. This is a baseline result, and we identified many possibilities for improvement. These entities were indexed in ElasticSearch and a user interface was developed on top of the index. This proof of concept was used in user requirement solicitation and evaluation with a group of end users. Feedback from this group indicated that there is a dire need for such a system, and that the first results are promising. Show less
Previous regulatory models for the online environment have been designed with a foundation and premise that users are fictional Homo Economicus – beings capable of optimizing all available... Show morePrevious regulatory models for the online environment have been designed with a foundation and premise that users are fictional Homo Economicus – beings capable of optimizing all available information into order to make sound decisions. Sometime these decisions will be rational and predictable. However, on other occasions users will make irrational, yet predictable mistakes; other times those errors will be unpredictably irrational. To overcome these shortcomings in rational economist models, behavioural economists like Daniel Kahneman, Amos Tversky and Richard Thaler and lawyers like Cass Sunstein have advocated using lessons from psychology to help people make better decisions. By deploying 'choice’ architecture to overcome less than rational decisions, Homo Sapiens can be 'nudged' to making better choices. Building from this foundation, our paper inquires what role big data may play in developing better regulation. For example, can insights from big data help to overcome erroneous assumptions that regulators may make about the way users rationally behave in online environments? What potential is there for harnessing ‘big data’ to provide insights into user behaviour? Can big data be used as an additional tool by lawmakers to improve regulatory settlements? Show less
