1,721,007 research outputs found
Diversity of play
Full text linkThe early days when digital games were new, harmless, and a niche are long gone. Today's games can simulate battlefields, predict disaster, and crash markets. We are faced with a diversity of play and the ubiquity of games, making them not only a popular medium, but the leading medium of our contemporary society. Based on the keynote lectures held at DiGRA2015, "Diversity of Play" provides a critical view on the current stage of digital games from a theoretic, artistic, and practical perspective by pointing towards the uncanny, the power of "unnatural" narratives, and the exceptions and uncertainties of digital ludic environments
On Game Structures
No full textOn Game Structures is an interdisciplinary platform for querying the logic of artistic, epistemological and economic moves and strategies. In art, science and philosophy, as in social praxis, every position is always an intersection of past moves. And every new move, in turn, alters the existing structure by altering the relationship between the structure’s constituent elements: time, space, rules, goals, and modes of interaction. This dynamic interpenetration of play – as emergent activity – and games – as coagulated structure – is, in this issue, explored in scholarly and artistic ways. Examples of the hybrid tropes the contributors engage with are liminoid social rites, playbour (the neoliberal fusion of play and labour), mathematical-musical recursion, Taqiyyah (the Islamic jurisprudence which embroils truth and falsity), porn-sports, and phantasmal ludicity.<br/
Integration of gene expression data with prior knowledge for network analysis and validation
No full textAbstract 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.</p
Connecting high-dimensional mRNA and miRNA expression data for binary medical classification problems
No full textIn 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. (c) 2013 Elsevier Ireland Ltd. All rights reserved
Matching of Matching-Graphs - A Novel Approach for Graph Classification
No full textDue to fast developments in data acquisition, we observe rapidly increasing amounts of data available in diverse areas. Simultaneously, we observe that in many applications the underlying data is inherently complex, making graphs a very useful and adequate data structure for formal representation. A large amount of graph based methods for pattern recognition have been proposed. Many of these methods actually rely on graph matching. In the present paper a novel encoding of graph matching information is proposed. The idea of this encoding is to formalize the stable cores of specific classes by means of graphs. In an empirical evaluation we show that it can be highly beneficial to focus on these stable parts of graphs during graph classification
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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