167,529 research outputs found
Understanding network concepts in modules
<p>Abstract</p> <p>Background</p> <p>Network concepts are increasingly used in biology and genetics. For example, the clustering coefficient has been used to understand network architecture; the connectivity (also known as degree) has been used to screen for cancer targets; and the topological overlap matrix has been used to define modules and to annotate genes. Dozens of potentially useful network concepts are known from graph theory.</p> <p>Results</p> <p>Here we study network concepts in special types of networks, which we refer to as approximately factorizable networks. In these networks, the pairwise connection strength (adjacency) between 2 network nodes can be factored into node specific contributions, named node 'conformity'. The node conformity turns out to be highly related to the connectivity. To provide a formalism for relating network concepts to each other, we define three types of network concepts: fundamental-, conformity-based-, and approximate conformity-based concepts. Fundamental concepts include the standard definitions of connectivity, density, centralization, heterogeneity, clustering coefficient, and topological overlap. The approximate conformity-based analogs of fundamental network concepts have several theoretical advantages. First, they allow one to derive simple relationships between seemingly disparate networks concepts. For example, we derive simple relationships between the clustering coefficient, the heterogeneity, the density, the centralization, and the topological overlap. The second advantage of approximate conformity-based network concepts is that they allow one to show that fundamental network concepts can be approximated by simple functions of the connectivity in module networks.</p> <p>Conclusion</p> <p>Using protein-protein interaction, gene co-expression, and simulated data, we show that a) many networks comprised of module nodes are approximately factorizable and b) in these types of networks, simple relationships exist between seemingly disparate network concepts. Our results are implemented in freely available R software code, which can be downloaded from the following webpage: <url>http://www.genetics.ucla.edu/labs/horvath/ModuleConformity/ModuleNetworks</url></p
On functional module detection in metabolic networks
Functional modules of metabolic networks are essential for understanding the metabolism of an organism as a whole. With the vast amount of experimental data and the construction of complex and large-scale, often genome-wide, models, the computer-aided identification of functional modules becomes more and more important. Since steady states play a key role in biology, many methods have been developed in that context, for example, elementary flux modes, extreme pathways, transition invariants and place invariants. Metabolic networks can be studied also from the point of view of graph theory, and algorithms for graph decomposition have been applied for the identification of functional modules. A prominent and currently intensively discussed field of methods in graph theory addresses the Q-modularity. In this paper, we recall known concepts of module detection based on the steady-state assumption, focusing on transition-invariants (elementary modes) and their computation as minimal solutions of systems of Diophantine equations. We present the Fourier-Motzkin algorithm in detail. Afterwards, we introduce the Q-modularity as an example for a useful non-steady-state method and its application to metabolic networks. To illustrate and discuss the concepts of invariants and Q-modularity, we apply a part of the central carbon metabolism in potato tubers (Solanum tuberosum) as running example. The intention of the paper is to give a compact presentation of known steady-state concepts from a graph-theoretical viewpoint in the context of network decomposition and reduction and to introduce the application of Q-modularity to metabolic Petri net models
Cross-talk and interference enhance information capacity of a signaling pathway
A recurring motif in gene regulatory networks is transcription factors (TFs)
that regulate each other, and then bind to overlapping sites on DNA, where they
interact and synergistically control transcription of a target gene. Here, we
suggest that this motif maximizes information flow in a noisy network. Gene
expression is an inherently noisy process due to thermal fluctuations and the
small number of molecules involved. A consequence of multiple TFs interacting
at overlapping binding-sites is that their binding noise becomes correlated.
Using concepts from information theory, we show that in general a signaling
pathway transmits more information if 1) noise of one input is correlated with
that of the other, 2) input signals are not chosen independently. In the case
of TFs, the latter criterion hints at up-stream cross-regulation. We
demonstrate these ideas for competing TFs and feed-forward gene regulatory
modules, and discuss generalizations to other signaling pathways. Our results
challenge the conventional approach of treating biological noise as
uncorrelated fluctuations, and present a systematic method for understanding TF
cross-regulation networks either from direct measurements of binding noise, or
bioinformatic analysis of overlapping binding-sites.Comment: 28 pages, 5 figure
Visual Question Answering: A Survey of Methods and Datasets
Visual Question Answering (VQA) is a challenging task that has received
increasing attention from both the computer vision and the natural language
processing communities. Given an image and a question in natural language, it
requires reasoning over visual elements of the image and general knowledge to
infer the correct answer. In the first part of this survey, we examine the
state of the art by comparing modern approaches to the problem. We classify
methods by their mechanism to connect the visual and textual modalities. In
particular, we examine the common approach of combining convolutional and
recurrent neural networks to map images and questions to a common feature
space. We also discuss memory-augmented and modular architectures that
interface with structured knowledge bases. In the second part of this survey,
we review the datasets available for training and evaluating VQA systems. The
various datatsets contain questions at different levels of complexity, which
require different capabilities and types of reasoning. We examine in depth the
question/answer pairs from the Visual Genome project, and evaluate the
relevance of the structured annotations of images with scene graphs for VQA.
Finally, we discuss promising future directions for the field, in particular
the connection to structured knowledge bases and the use of natural language
processing models.Comment: 25 page
Engaging sport students in assessment and formative feedback
Sport as a discipline in higher education is grappling with the challenge of providing authentic and relevant assessment that engages students in their learning. The centrality of assessment to the student experience is now well accepted within the research literature (Brown and Knight, 1994; Rust, 2002). In particular, formative assessment, or assessment that creates feedback to support future teaching and learning experiences, can be a powerful tool for enhancing learning (see Black and Wiliam, 1998). Given that feedback is most effective if it is considered or reflected upon, one of the key challenges is to actively engage sport students in formative assessment processes. This guide offers advice in designing and facilitating sport students’ involvement in assessment and enhancing their engagement with the feedback they receive. The aim is to support sport programme teams by taking a pragmatic approach, combining a clear academic rationale based on assessment for learning principles with case study examples of successful formative assessment exercises emphasising innovative approaches to giving feedback. The guide consists of three key sections focused on: 1) Providing staff in HLST with background knowledge of formative assessment and formative feedback and how it relates to their subject. 2) Providing case study examples of how to effectively engage sport students with assessment feedback so that it feeds-forward to aid learning. 3) Providing a resource of references and sources of support for tutors wishing to further their learning in this area
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Teaching in Context: Integrating Mathematical Thinking and Personal Development Planning into the Curriculum for Part-Time, Distance-Learning Engineering Students
This paper describes changes to the way mathematics is taught to engineering students at The Open University, moving away from service teaching via generic mathematics modules to incorporating mathematics teaching into the core engineering curriculum. Mathematics is taught in the context of engineering with the aim of reducing the emphasis on derivations and mathematical proofs and putting greater emphasis on understanding basic concepts and being able to create useful models. Mathematical methods are taught and practised, then extended and applied to different engineering contexts as students’ progress through modules, in order to develop students’ mathematical thinking and build confidence. Professional development planning has also been embedded into engineering teaching for improved context and relevance and a more integrated approach to assessment has been taken across the whole qualification
Perspective: network-guided pattern formation of neural dynamics
The understanding of neural activity patterns is fundamentally linked to an
understanding of how the brain's network architecture shapes dynamical
processes. Established approaches rely mostly on deviations of a given network
from certain classes of random graphs. Hypotheses about the supposed role of
prominent topological features (for instance, the roles of modularity, network
motifs, or hierarchical network organization) are derived from these
deviations. An alternative strategy could be to study deviations of network
architectures from regular graphs (rings, lattices) and consider the
implications of such deviations for self-organized dynamic patterns on the
network. Following this strategy, we draw on the theory of spatiotemporal
pattern formation and propose a novel perspective for analyzing dynamics on
networks, by evaluating how the self-organized dynamics are confined by network
architecture to a small set of permissible collective states. In particular, we
discuss the role of prominent topological features of brain connectivity, such
as hubs, modules and hierarchy, in shaping activity patterns. We illustrate the
notion of network-guided pattern formation with numerical simulations and
outline how it can facilitate the understanding of neural dynamics
Inviwo -- A Visualization System with Usage Abstraction Levels
The complexity of today's visualization applications demands specific
visualization systems tailored for the development of these applications.
Frequently, such systems utilize levels of abstraction to improve the
application development process, for instance by providing a data flow network
editor. Unfortunately, these abstractions result in several issues, which need
to be circumvented through an abstraction-centered system design. Often, a high
level of abstraction hides low level details, which makes it difficult to
directly access the underlying computing platform, which would be important to
achieve an optimal performance. Therefore, we propose a layer structure
developed for modern and sustainable visualization systems allowing developers
to interact with all contained abstraction levels. We refer to this interaction
capabilities as usage abstraction levels, since we target application
developers with various levels of experience. We formulate the requirements for
such a system, derive the desired architecture, and present how the concepts
have been exemplary realized within the Inviwo visualization system.
Furthermore, we address several specific challenges that arise during the
realization of such a layered architecture, such as communication between
different computing platforms, performance centered encapsulation, as well as
layer-independent development by supporting cross layer documentation and
debugging capabilities
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