Joost N. Kok

The FAIR Guiding Principles for scientific data management and stewardship

There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.

Unsupervised clustering with spiking neurons by sparse temporal coding and multilayer RBF networks

We demonstrate that spiking neural networks encoding information in the timing of single spikes are capable of computing and learning clusters from realistic data. We show how a spiking neural network based on spike-time coding and Hebbian learning can successfully perform unsupervised clustering on real-world data, and we demonstrate how temporal synchrony in a multilayer network can induce hierarchical clustering. We develop a temporal encoding of continuously valued data to obtain adjustable clustering capacity and precision with an efficient use of neurons: input variables are encoded in a population code by neurons with graded and overlapping sensitivity profiles. We also discuss methods for enhancing scale-sensitivity of the network and show how the induced synchronization of neurons within early RBF layers allows for the subsequent detection of complex clusters.

Handbook of Natural Computing

Natural Computing is the field of research that investigates both human-designed computing inspired by nature and computing taking place in nature, i.e., it investigates models and computational techniques inspired by nature and also it investigates phenomena taking place in nature in terms of information processing.Examples of the first strand of research covered by the handbook which has three volumesinclude neural computation inspired by the functioning of the brain; evolutionary computation inspired by Darwinian evolution of species; cellular automata inspired by intercellular communication; swarm intelligence inspired by the behavior of groups of organisms; artificial immune systems inspired by the natural immune system; artificial life systems inspired by the properties of natural life in general; membrane computing inspired by the compartmentalized ways in which cells process information; and amorphous computing inspired by morphogenesis. Other examples of natural-computing paradigms are molecular computing and quantum computing, where the goal is to replace traditional electronic hardware, e.g., by bioware in molecular computing. In molecular computing, data are encoded as biomolecules and then molecular biology tools are used to transform the data, thus performing computations. In quantum computing, one exploits quantum-mechanical phenomena to perform computations and secure communications more efficiently than classical physics and, hence, traditional hardware allows.The second strand of research covered by the handbook, computation taking place in nature, is represented by investigations into, among others, the computational nature of self-assembly, which lies at the core of nanoscience, the computational nature of developmental processes, the computational nature of biochemical reactions, the computational nature of bacterial communication, the computational nature of brain processes, and the systems biology approach to bionetworks where cellular processes are treated in terms of communication and interaction, and, hence, in terms of computation.We are now witnessing exciting interaction between computer science and the natural sciences. While the natural sciences are rapidly absorbing notions, techniques and methodologies intrinsic to information processing, computer science is adapting and extending its traditional notion of computation, and computational techniques, to account for computation taking place in nature around us. Natural Computing is an important catalyst for this two-way interaction, and this three-volume handbook is a major record of this important development.

Clinical subtypes of Parkinson's disease.

The clinical heterogeneity of Parkinsons disease (PD) may point at the existence of subtypes. Because subtypes likely reflect distinct underlying etiologies, their identification may facilitate future genetic and pharmacotherapeutic studies. Aim of this study was to identify subtypes by a data‐driven approach applied to a broad spectrum of motor and nonmotor features of PD. Data of motor and nonmotor PD symptoms were collected in 802 patients in two different European prevalent cohorts. A model‐based cluster analysis was conducted on baseline data of 344 patients of a Dutch cohort (PROPARK). Reproducibility of these results was tested in data of the second annual assessment of the same cohort and validated in an independent Spanish cohort (ELEP) of 357 patients. The subtypes were subsequently characterized on clinical and demographic variables. Four similar PD subtypes were identified in two different populations and are largely characterized by differences in the severity of nondopaminergic features and motor complications: Subtype 1 was mildly affected in all domains, Subtype 2 was predominantly characterized by severe motor complications, Subtype 3 was affected mainly on nondopaminergic domains without prominent motor complications, while Subtype 4 was severely affected on all domains. The subtypes had largely similar mean disease durations (nonsignificant differences between three clusters) but showed considerable differences with respect to their association with demographic and clinical variables. In prevalent disease, PD subtypes are largely characterized by the severity of nondopaminergic features and motor complications and likely reflect complex interactions between disease mechanisms, treatment, aging, and gender.

The Gaston Tool for Frequent Subgraph Mining

Given a database of graphs, structure mining algorithms search for all substructures that satisfy constraints such as minimum frequency, minimum confidence, minimum interest and maximum frequency. In order to make frequent subgraph mining more efficient, we propose to search with steps of increasing complexity. We present the GrAph/Sequence/Tree extractiON (Gaston) tool that implements this idea by searching first for frequent paths, then frequent free trees and finally cyclic graphs. We give results on large molecular databases.

The Identification of Parkinson's Disease Subtypes Using Cluster Analysis: A Systematic Review

The clinical variability between patients with Parkinsons disease (PD) may point at the existence of subtypes of the disease. Identification of subtypes is important, since a focus on homogeneous groups may enhance the chance of success of research on mechanisms of disease and may also lead to tailored treatment strategies. Cluster analysis (CA) is an objective method to classify patients into subtypes. We systematically reviewed the methodology and results of CA studies in PD to gain a better understanding of the robustness of identified subtypes. We found seven studies that fulfilled the inclusion criteria. Studies were limited by incomplete reporting and methodological limitations. Differences between studies rendered comparisons of the results difficult. However, it appeared that studies which applied a comparable design identified similar subtypes. The cluster profiles “old age‐at‐onset and rapid disease progression” and “young age‐at‐onset and slow disease progression” emerged from the majority of studies. Other cluster profiles were less consistent across studies. Future studies with a rigorous study design that is standardized with respect to the included variables, data processing, and CA technique may advance the knowledge on subtypes in PD.© 2010 Movement Disorder Society

Genetic Programming for data classification: partitioning the search space

When Genetic Programming is used to evolve decision trees for data classification, search spaces tend to become extremely large. We present several methods using techniques from the field of machine learning to refine and thereby reduce the search space sizes for decision tree evolvers. We will show that these refinement methods improve the classification performance of our algorithms.

Orgy in the Computer: Multi-Parent Reproduction in Genetic Algorithms

In this paper we investigate the phenomenon of multi-parent reproduction, i.e. we study recombination mechanisms where an arbitrary n>1 number of parents participate in creating children. In particular, we discuss scanning crossover that generalizes the standard uniform crossover and diagonal crossover that generalizes 1-point crossover, and study the effects of different number of parents on the GA behavior. We conduct experiments on tough function optimization problems and observe that by multi-parent operators the performance of GAs can be enhanced significantly. We also give a theoretical foundation by showing how these operators work on distributions.

The Failure of Failures in a Paradigm for Asynchronous Communication

We develop a general framework for a variety of concurrent languages all based on asynchronous communication, like data flow, concurrent logic, concurrent constraint languages and CSP with asynchronous channels. The main characteristic of these languages is that processes interact by reading and modifying the state of some common data structure. We abstract from the specific features of the various communication mechanisms by means of a uniform language where actions are interpreted as partially defined transformations on an abstract set of states. Suspension is modelled by an action being undefined in a state. The languages listed above can be seen as instances of our paradigm, and can be obtained by fixing a specific set of states and interpretation of the actions.

Denotational semantics of a parallel object-oriented language

Abstract A denotational model is presented for the language POOL, a parallel object-oriented language. It is a syntactically simplified version of POOL-T, a language that is actually used to write programs for a parallel machine. The most important aspect of this language is that it describes a system as a collection of communicating objects that all have internal activities which are executed in parallel. To describe the semantics of this language we construct a mathematical domain of processes. This domain is obtained as a solution of a reflexive domain equation over a category of complete metric spaces. A new technique is developed to solve a wide class of such equations, including function space constructions. The desired domain is obtained as the fixed point of a contracting functor implicit in the equation. The domain is sufficiently rich to allow a fully compositional definition of the language constructs in POOL, including concepts such as object creation and method invocation by messages. The semantic equations give a meaning to each syntactic construct depending on the POOL object executing the construct, the environment constituted by the declarations, and a continuation, representing the actions to be performed after the execution of the current construct. After the process representing the execution of an entire program is constructed, a yield function can extract the set of possible execution sequences from it. A preliminary discussion is provided on how to deal with fairness. Full mathematical details are supplied, with the exception of the general domain construction, which is described elsewhere.