127,044 research outputs found
Self-Organizing Time Map: An Abstraction of Temporal Multivariate Patterns
This paper adopts and adapts Kohonen's standard Self-Organizing Map (SOM) for
exploratory temporal structure analysis. The Self-Organizing Time Map (SOTM)
implements SOM-type learning to one-dimensional arrays for individual time
units, preserves the orientation with short-term memory and arranges the arrays
in an ascending order of time. The two-dimensional representation of the SOTM
attempts thus twofold topology preservation, where the horizontal direction
preserves time topology and the vertical direction data topology. This enables
discovering the occurrence and exploring the properties of temporal structural
changes in data. For representing qualities and properties of SOTMs, we adapt
measures and visualizations from the standard SOM paradigm, as well as
introduce a measure of temporal structural changes. The functioning of the
SOTM, and its visualizations and quality and property measures, are illustrated
on artificial toy data. The usefulness of the SOTM in a real-world setting is
shown on poverty, welfare and development indicators
Patterns of subnet usage reveal distinct scales of regulation in the transcriptional regulatory network of Escherichia coli
The set of regulatory interactions between genes, mediated by transcription
factors, forms a species' transcriptional regulatory network (TRN). By
comparing this network with measured gene expression data one can identify
functional properties of the TRN and gain general insight into transcriptional
control. We define the subnet of a node as the subgraph consisting of all nodes
topologically downstream of the node, including itself. Using a large set of
microarray expression data of the bacterium Escherichia coli, we find that the
gene expression in different subnets exhibits a structured pattern in response
to environmental changes and genotypic mutation. Subnets with less changes in
their expression pattern have a higher fraction of feed-forward loop motifs and
a lower fraction of small RNA targets within them. Our study implies that the
TRN consists of several scales of regulatory organization: 1) subnets with more
varying gene expression controlled by both transcription factors and
post-transcriptional RNA regulation, and 2) subnets with less varying gene
expression having more feed-forward loops and less post-transcriptional RNA
regulation.Comment: 14 pages, 8 figures, to be published in PLoS Computational Biolog
Context-aware visual exploration of molecular databases
Facilitating the visual exploration of scientific data has
received increasing attention in the past decade or so. Especially
in life science related application areas the amount
of available data has grown at a breath taking pace. In this
paper we describe an approach that allows for visual inspection
of large collections of molecular compounds. In
contrast to classical visualizations of such spaces we incorporate
a specific focus of analysis, for example the outcome
of a biological experiment such as high throughout
screening results. The presented method uses this experimental
data to select molecular fragments of the underlying
molecules that have interesting properties and uses the
resulting space to generate a two dimensional map based
on a singular value decomposition algorithm and a self organizing
map. Experiments on real datasets show that
the resulting visual landscape groups molecules of similar
chemical properties in densely connected regions
Learning and Interpreting Multi-Multi-Instance Learning Networks
We introduce an extension of the multi-instance learning problem where
examples are organized as nested bags of instances (e.g., a document could be
represented as a bag of sentences, which in turn are bags of words). This
framework can be useful in various scenarios, such as text and image
classification, but also supervised learning over graphs. As a further
advantage, multi-multi instance learning enables a particular way of
interpreting predictions and the decision function. Our approach is based on a
special neural network layer, called bag-layer, whose units aggregate bags of
inputs of arbitrary size. We prove theoretically that the associated class of
functions contains all Boolean functions over sets of sets of instances and we
provide empirical evidence that functions of this kind can be actually learned
on semi-synthetic datasets. We finally present experiments on text
classification, on citation graphs, and social graph data, which show that our
model obtains competitive results with respect to accuracy when compared to
other approaches such as convolutional networks on graphs, while at the same
time it supports a general approach to interpret the learnt model, as well as
explain individual predictions.Comment: JML
Isolating and Quantifying the Role of Developmental Noise in Generating Phenotypic Variation
Genotypic variation, environmental variation, and their interaction may produce variation in the developmental process and cause phenotypic differences among individuals. Developmental noise, which arises during development from stochasticity in cellular and molecular processes when genotype and environment are fixed, also contributes to phenotypic variation. While evolutionary biology has long focused on teasing apart the relative contribution of genes and environment to phenotypic variation, our understanding of the role of developmental noise has lagged due to technical difficulties in directly measuring the contribution of developmental noise. The influence of developmental noise is likely underestimated in studies of phenotypic variation due to intrinsic mechanisms within organisms that stabilize phenotypes and decrease variation. Since we are just beginning to appreciate the extent to which phenotypic variation due to stochasticity is potentially adaptive, the contribution of developmental noise to phenotypic variation must be separated and measured to fully understand its role in evolution. Here, we show that variation in the component of the developmental process corresponding to environmental and genetic factors (here treated together as a unit called the LALI-type) versus the contribution of developmental noise, can be distinguished for leopard gecko (Eublepharis macularius) head color patterns using mathematical simulations that model the role of random variation (corresponding to developmental noise) in patterning. Specifically, we modified the parameters of simulations corresponding to variation in the LALI-type to generate the full range of phenotypic variation in color pattern seen on the heads of eight leopard geckos. We observed that over the range of these parameters, variation in color pattern due to LALI-type variation exceeds that due to developmental noise in the studied gecko cohort. However, the effect of developmental noise on patterning is also substantial. Our approach addresses one of the major goals of evolutionary biology: to quantify the role of stochasticity in shaping phenotypic variation
Complex Systems Science: Dreams of Universality, Reality of Interdisciplinarity
Using a large database (~ 215 000 records) of relevant articles, we
empirically study the "complex systems" field and its claims to find universal
principles applying to systems in general. The study of references shared by
the papers allows us to obtain a global point of view on the structure of this
highly interdisciplinary field. We show that its overall coherence does not
arise from a universal theory but instead from computational techniques and
fruitful adaptations of the idea of self-organization to specific systems. We
also find that communication between different disciplines goes through
specific "trading zones", ie sub-communities that create an interface around
specific tools (a DNA microchip) or concepts (a network).Comment: Journal of the American Society for Information Science and
Technology (2012) 10.1002/asi.2264
Evidence for Information Processing in the Brain
Many cognitive and neuroscientists attempt to assign biological functions to brain structures. To achieve this end, scientists perform experiments that relate the physical properties of brain structures to organism-level abilities, behaviors, and environmental stimuli. Researchers make use of various measuring instruments and methodological techniques to obtain this kind of relational evidence, ranging from single-unit electrophysiology and optogenetics to whole brain functional MRI. Each experiment is intended to identify brain function. However, seemingly independent of experimental evidence, many cognitive scientists, neuroscientists, and philosophers of science assume that the brain processes information as a scientific fact. In this work we analyze categories of relational evidence and find that although physical features of specific brain areas selectively covary with external stimuli and abilities, and that the brain shows reliable causal organization, there is no direct evidence supporting the claim that information processing is a natural function of the brain. We conclude that the belief in brain information processing adds little to the science of cognitive science and functions primarily as a metaphor for efficient communication of neuroscientific data
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