11,947 research outputs found
A conceptual approach to gene expression analysis enhanced by visual analytics
The analysis of gene expression data is a complex task for biologists wishing to understand the role of genes in the formation of diseases such as cancer. Biologists need greater support when trying to discover, and comprehend, new relationships within their data. In this paper, we describe an approach to the analysis of gene expression data where overlapping groupings are generated by Formal Concept Analysis and interactively analyzed in a tool called CUBIST. The CUBIST workflow involves querying a semantic database and converting the result into a formal context, which can be simplified to make it manageable, before it is visualized as a concept lattice and associated charts
Allelomimesis as universal clustering mechanism for complex adaptive systems
Animal and human clusters are complex adaptive systems and many are organized
in cluster sizes that obey the frequency-distribution . Exponent describes the relative abundance of the cluster
sizes in a given system. Data analyses have revealed that real-world clusters
exhibit a broad spectrum of -values, . We show that allelomimesis is a
fundamental mechanism for adaptation that accurately explains why a broad
spectrum of -values is observed in animate, human and inanimate cluster
systems. Previous mathematical models could not account for the phenomenon.
They are hampered by details and apply only to specific systems such as cities,
business firms or gene family sizes. Allelomimesis is the tendency of an
individual to imitate the actions of its neighbors and two cluster systems
yield different values if their component agents display different
allelomimetic tendencies. We demonstrate that allelomimetic adaptation are of
three general types: blind copying, information-use copying, and non-copying.
Allelomimetic adaptation also points to the existence of a stable cluster size
consisting of three interacting individuals.Comment: 8 pages, 5 figures, 2 table
Spreading dynamics on spatially constrained complex brain networks
The study of dynamical systems defined on complex networks provides a natural framework with which to investigate myriad features of neural dynamics and has been widely undertaken. Typically, however, networks employed in theoretical studies bear little relation to the spatial embedding or connectivity of the neural networks that they attempt to replicate. Here, we employ detailed neuroimaging data to define a network whose spatial embedding represents accurately the folded structure of the cortical surface of a rat brain and investigate the propagation of activity over this network under simple spreading and connectivity rules. By comparison with standard network models with the same coarse statistics, we show that the cortical geometry influences profoundly the speed of propagation of activation through the network. Our conclusions are of high relevance to the theoretical modelling of epileptic seizure events and indicate that such studies which omit physiological network structure risk simplifying the dynamics in a potentially significant way
Visualizing and Interacting with Concept Hierarchies
Concept Hierarchies and Formal Concept Analysis are theoretically well
grounded and largely experimented methods. They rely on line diagrams called
Galois lattices for visualizing and analysing object-attribute sets. Galois
lattices are visually seducing and conceptually rich for experts. However they
present important drawbacks due to their concept oriented overall structure:
analysing what they show is difficult for non experts, navigation is
cumbersome, interaction is poor, and scalability is a deep bottleneck for
visual interpretation even for experts. In this paper we introduce semantic
probes as a means to overcome many of these problems and extend usability and
application possibilities of traditional FCA visualization methods. Semantic
probes are visual user centred objects which extract and organize reduced
Galois sub-hierarchies. They are simpler, clearer, and they provide a better
navigation support through a rich set of interaction possibilities. Since probe
driven sub-hierarchies are limited to users focus, scalability is under control
and interpretation is facilitated. After some successful experiments, several
applications are being developed with the remaining problem of finding a
compromise between simplicity and conceptual expressivity
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