Machine learning applications in proteomics research: How the past can boost the future

Machine learning is a subdiscipline within artificial intelligence that focuses on algorithms that allow computers to learn solving a (complex) problem from existing data. This ability can be used to generate a solution to a particularly intractable problem, given that enough data are available to train and subsequently evaluate an algorithm on. Since MS-based proteomics has no shortage of complex problems, and since publicly available data are becoming available in ever growing amounts, machine learning is fast becoming a very popular tool in the field. We here therefore present an overview of the different applications of machine learning in proteomics that together cover nearly the entire wet- and dry-lab workflow, and that address key bottlenecks in experiment planning and design, as well as in data processing and analysis.acceptedVersio

40 Years Theory and Model at Wageningen UR

"Theorie en model" zo luidde de titel van de inaugurele rede van CT de Wit (1968). Reden genoeg voor een (theoretische) terugblik op zijn wer

Property Rights, Land Fragmentation and the Emerging Structure of Agriculture in Central and Eastern European Countries

This paper offers an overview of land reform processes in the CEECs and their outcomes and impacts and analyzes current and emerging structures in rural areas. Different types of land consolidation are defined and their potential impacts are assessed. The paper then looks in depth at land consolidation processes, especially in the context of land management, and outlines preconditions and cornerstones for various approaches. Environmental aspects and principles for land funds and land banking are also drawn in. The paper argues the need for an integrated and sustainable rural development which includes a role for land consolidation.Transition economies, land tenure, land fragmentation, land consolidation, rural development, Land Economics/Use,

Message in a bottle: learning our way out of unsustainability

Inaugural lecture of Prof. dr. ir. Arjen E.J. Wals upon taking up the posts of Professor of Social Learning and Sustainable Development, and UNESCO Chair at Wageningen University on May 27th 2010. Lecture about the consequences of unsustainable usage of plastics

The Confluence of Intersubjectivity and Dialogue in Postmodern Organizational Workgroups

Nascent revival of dialogue is struggling to reach its potential within the postmodern organizational milieu. Concurrently, interpersonal intersubjectivity has steadily been de-pathologized, via reassessments of countertransference in the psychoanalytic sphere, allowing exploration of its utility in other domains of relational process. Effective use of dialogue is critical and foundational to developing meaningful and sustainable enterprise in the immediate future. Despite the risks, intentionally explored intersubjectivity is a powerful tool to enrich the container of dialogue. This paper qualitatively explores the literature on intersubjectivity and dialogue with an hermeneutic approach to discern the implications of their convergence for collaborative workgroups in emergent enterprise

The Four Waves of Systems Thinking

This Handbook is about the past, present, and future of systems thinking. It captures the history of systems thinking over its first three ‘waves,’ which are thought of as significant paradigmatic time periods in the history of the field. It then introduces a (possible) emerging fourth wave. Herein, we review the first three waves, as they have been written about in depth before, and dedicate more space to describing the fourth wave, as this is likely to be new to many readers. We cover all four waves as an entree to the many chapters, which were both recommended by an International Advisory Board (listed and thanked in the front material of this book), and written by esteemed invited authors. These chapters aptly describe the various frameworks that characterize the different waves; and notably include how those frameworks have continued to evolve since their origin

Participative Approaches to Hedgerow Conservation

This thesis demonstrates how systems ideas and grounded theory have been applied to provide a broader approach to researching hedgerows in England, drawing on the idea that holistic thinking brings together different people’s relationships with hedgerows and with each other concerning hedgerows. The cultural dimensions of hedgerows and their implications for future hedged landscapes were investigated through the collection and exploration of different groups perspectives - public, farmers and experts - in England and Canada, using a diversity of primary and secondary data sources. English hedgerows were important to all groups. Everyone liked hedged landscapes for aesthetic, visual and wildlife reasons. They were important for the way they break up the landscape; provide signs of the changing seasons; their sense of mystery and intimacy; their connections with the past and childhood memories. They are also seen as part of England’s history and national identity. Such cultural identity was absent in the Canadian data. However, some groups also held a rational or objective view which was dominant over this subjective or emotional view and which affects where they draw the boundaries to their systems of interest. Farmers were most concerned with their farms (and the hedgerows they owned) as a business, while experts dealt mainly with the ecological aspects of hedgerows. There was found to be little awareness of others groups views with different groups seeing the same action in very different ways. Even where there was contact between farmers and experts, there could be a lack of trust. Finally, it is noted that policy and practice towards hedgerows have ignored many of these relationships and that the approach used here offers opportunities to examine the different systems of interest

Mining Predictive Patterns and Extension to Multivariate Temporal Data

An important goal of knowledge discovery is the search for patterns in the data that can help explaining its underlying structure. To be practically useful, the discovered patterns should be novel (unexpected) and easy to understand by humans. In this thesis, we study the problem of mining patterns (defining subpopulations of data instances) that are important for predicting and explaining a specific outcome variable. An example is the task of identifying groups of patients that respond better to a certain treatment than the rest of the patients. We propose and present efficient methods for mining predictive patterns for both atemporal and temporal (time series) data. Our first method relies on frequent pattern mining to explore the search space. It applies a novel evaluation technique for extracting a small set of frequent patterns that are highly predictive and have low redundancy. We show the benefits of this method on several synthetic and public datasets. Our temporal pattern mining method works on complex multivariate temporal data, such as electronic health records, for the event detection task. It first converts time series into time-interval sequences of temporal abstractions and then mines temporal patterns backwards in time, starting from patterns related to the most recent observations. We show the benefits of our temporal pattern mining method on two real-world clinical tasks