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Agent-based semiology is a powerful simulation and prediction environment for pedestrian simulation that allows for accurate balancing of complexity. Here, we describe a framework to simulate increasing behavioural interactivity between agents via agent-based modeling, together with a statistical approach to make the results amenable to a quantitative and automated analysis. That approach borrows ideas from crowd simulation and spatial statistics, notably fitting of Poisson processes, and computer graphics. The described process can simply be thought of as that of approaching an observed pattern by an overlay or additive mixture of grey-scale images each of which are distance transforms of physical objects. Thus, we describe the observed pattern in terms of interactions of spatial features which are akin to traditional BIM tags. We thus arrive at a remarkably concise prediction of the simulation outcome. The benefits of this simulation speedup is, on the one hand, to allow for higher optimization throughput, and on the other hand, to provide designers with quantitative feedback about the impact of their design on the simulation outcome.

As modern biotechnologies advance, it has become increasingly frequent that different modalities of high-dimensional molecular data (termed “omics” data in this paper), such as gene expression, methylation, and copy number, are collected from the same patient cohort to predict the clinical outcome. While prediction based on omics data has been widely studied in the last fifteen years, little has been done in the statistical literature on the integration of multiple omics modalities to select a subset of variables for prediction, which is a critical task in personalized medicine. In this paper, we propose a simple penalized regression method to address this problem by assigning different penalty factors to different data modalities for feature selection and prediction. The penalty factors can be chosen in a fully data-driven fashion by cross-validation or by taking practical considerations into account. In simulation studies, we compare the prediction performance of our approach, called IPF-LASSO (Integrative LASSO with Penalty Factors) and implemented in the R package ipflasso, with the standard LASSO and sparse group LASSO. The use of IPF-LASSO is also illustrated through applications to two real-life cancer datasets. All data and codes are available on the companion website to ensure reproducibility.

In modern molecular biology, high-throughput experiments allow the simultaneous study of expression levels of thousands of biopolymers such as mRNAs, miRNAs or proteins. A typical goal of such experiments is to find molecular signatures that can distinguish between different types of tissue or that can predict a therapy outcome. While research typically focuses on just one type of molecular features of a gene, e.g. mRNA expression levels, there is increasing interest in the study of several types of features in parallel, i.e. within the same biological samples. In this manuscript, we aim at elucidating the peculiarities of the combination of mRNA and miRNA expression levels in binary medical classification problems by proposing and comparing different methodologies. The ensuing combined classifiers are evaluated within a simulation study. They are based on linear discriminant analysis, linear support vector machines, as well as on a non-linear classifier. In addition, we compare the performance of the different approaches on real expression data sets. In the simulations as well as in the real data sets, in most though not all cases the combinations yield equal or higher accuracy than the individual classifiers based on only one type of features.

Recent online gaming developments and para-gaming environments, i.e., the social software tools to communicate with fellow gamers, report, discuss and disseminate assets and experience, strongly resemble social media like Facebook or Twitter. It has therefore been suggested that games like World of Warcraft, Little Big Planet, The Godfather, or The Secret World should be called “social games.” Privacy and security is an issue in these social games, as the players of these games do often not realize that they inhabit environments that have real estate outside the safe borders of the Magic Circle. The games companies harvest information about the players in ways that are far from transparent. The author will present examples of data mining and harvesting of data within a playful environment, analyze code segments that implement data collection, and suggest methods of refusal, sabotage, or disclosure of breeches of contract.

Background Reconstruction of protein-protein interaction or metabolic networks based on expression data often involves in silico predictions, while on the other hand, there are unspecific networks of in vivo interactions derived from knowledge bases. We analyze networks designed to come as close as possible to data measured in vivo, both with respect to the set of nodes which were taken to be expressed in experiment as well as with respect to the interactions between them which were taken from manually curated databases Results A signaling network derived from the TRANSPATH database and a metabolic network derived from KEGG LIGAND are each filtered onto expression data from breast cancer (SAGE) considering different levels of restrictiveness in edge and vertex selection. We perform several validation steps, in particular we define pathway over-representation tests based on refined null models to recover functional modules. The prominent role of the spindle checkpoint-related pathways in breast cancer is exhibited. High-ranking key nodes cluster in functional groups retrieved from literature. Results are consistent between several functional and topological analyses and between signaling and metabolic aspects. Conclusions This construction involved as a crucial step the passage to a mammalian protein identifier format as well as to a reaction-based semantics of metabolism. This yielded good connectivity but also led to the need to perform benchmark tests to exclude loss of essential information. Such validation, albeit tedious due to limitations of existing methods, turned out to be informative, and in particular provided biological insights as well as information on the degrees of coherence of the networks despite fragmentation of experimental data. Key node analysis exploited the networks for potentially interesting proteins in view of drug target prediction.