537,653 research outputs found
The evolution of carrying capacity in constrained and expanding tumour cell populations
Cancer cells are known to modify their micro-environment such that it can
sustain a larger population, or, in ecological terms, they construct a niche
which increases the carrying capacity of the population. It has however been
argued that niche construction, which benefits all cells in the tumour, would
be selected against since cheaters could reap the benefits without paying the
cost. We have investigated the impact of niche specificity on tumour evolution
using an individual based model of breast tumour growth, in which the carrying
capacity of each cell consists of two components: an intrinsic,
subclone-specific part and a contribution from all neighbouring cells. Analysis
of the model shows that the ability of a mutant to invade a resident population
depends strongly on the specificity. When specificity is low selection is
mostly on growth rate, while high specificity shifts selection towards
increased carrying capacity. Further, we show that the long-term evolution of
the system can be predicted using adaptive dynamics. By comparing the results
from a spatially structured vs.\ well-mixed population we show that spatial
structure restores selection for carrying capacity even at zero specificity,
which a poses solution to the niche construction dilemma. Lastly, we show that
an expanding population exhibits spatially variable selection pressure, where
cells at the leading edge exhibit higher growth rate and lower carrying
capacity than those at the centre of the tumour.Comment: Major revisions compared to previous version. The paper is now aimed
at tumour modelling. We now start out with an agent-based model for which we
derive a mean-field ODE-model. The ODE-model is further analysed using the
theory of adaptive dynamic
Factorial graphical lasso for dynamic networks
Dynamic networks models describe a growing number of important scientific
processes, from cell biology and epidemiology to sociology and finance. There
are many aspects of dynamical networks that require statistical considerations.
In this paper we focus on determining network structure. Estimating dynamic
networks is a difficult task since the number of components involved in the
system is very large. As a result, the number of parameters to be estimated is
bigger than the number of observations. However, a characteristic of many
networks is that they are sparse. For example, the molecular structure of genes
make interactions with other components a highly-structured and therefore
sparse process.
Penalized Gaussian graphical models have been used to estimate sparse
networks. However, the literature has focussed on static networks, which lack
specific temporal constraints. We propose a structured Gaussian dynamical
graphical model, where structures can consist of specific time dynamics, known
presence or absence of links and block equality constraints on the parameters.
Thus, the number of parameters to be estimated is reduced and accuracy of the
estimates, including the identification of the network, can be tuned up. Here,
we show that the constrained optimization problem can be solved by taking
advantage of an efficient solver, logdetPPA, developed in convex optimization.
Moreover, model selection methods for checking the sensitivity of the inferred
networks are described. Finally, synthetic and real data illustrate the
proposed methodologies.Comment: 30 pp, 5 figure
Learning Dynamic Feature Selection for Fast Sequential Prediction
We present paired learning and inference algorithms for significantly
reducing computation and increasing speed of the vector dot products in the
classifiers that are at the heart of many NLP components. This is accomplished
by partitioning the features into a sequence of templates which are ordered
such that high confidence can often be reached using only a small fraction of
all features. Parameter estimation is arranged to maximize accuracy and early
confidence in this sequence. Our approach is simpler and better suited to NLP
than other related cascade methods. We present experiments in left-to-right
part-of-speech tagging, named entity recognition, and transition-based
dependency parsing. On the typical benchmarking datasets we can preserve POS
tagging accuracy above 97% and parsing LAS above 88.5% both with over a
five-fold reduction in run-time, and NER F1 above 88 with more than 2x increase
in speed.Comment: Appears in The 53rd Annual Meeting of the Association for
Computational Linguistics, Beijing, China, July 201
Standard Information Model for Meta-Data
This document provides a detailed and explanatory description of the Standard Information Model for Meta-Data (SIM) which constitutes an intrinsic part of the Spatial Information Platform (SIP) of the EU FP7 project SWITCH-ON (Sharing Water-related Information to Tackle Changes in the Hydrosphere â for Operational Needs).
Although widely adopted information models for the description of data and services do exist (e.g. ISO19115 (2003) and ISO 19119 (2005), the Standard Information Model of the SIP is not solely based on one of these standards. Instead of defining one fixed information model that is based on a selection of particular meta(data) standards or profiles, the Standard Information Model of the SIP has been tailored to the actual information needs of the SIP, auxiliary services, and tools as well as its end users (product developers and researchers working in the virtual water-science lab). Thereby, the concepts of the CKAN (Comprehensive Knowledge Archive Network) domain model as well as support for meta- (data) standards like Dublin Core, ISO 19115, etc., have been considered in the design of the SIM.
The design of the SIM follows therefore a graduated approach with the following three different levels of increasing extensibility and flexibility:
Relational Model
The relational model defines the outline for an object relational database model and supports the core business processes of the SIP.
Dynamic Tag Extensions
Dynamic tag extensions augment the relational model by user definable code lists and thus provide a simple yet powerful extension mechanism.
Dynamic Content Extensions
Dynamic Content Extensions form a mechanism to dynamically inject complex structured or semi-structured content in the SIM without the need to change the relational model
Deep Elastic Networks with Model Selection for Multi-Task Learning
In this work, we consider the problem of instance-wise dynamic network model
selection for multi-task learning. To this end, we propose an efficient
approach to exploit a compact but accurate model in a backbone architecture for
each instance of all tasks. The proposed method consists of an estimator and a
selector. The estimator is based on a backbone architecture and structured
hierarchically. It can produce multiple different network models of different
configurations in a hierarchical structure. The selector chooses a model
dynamically from a pool of candidate models given an input instance. The
selector is a relatively small-size network consisting of a few layers, which
estimates a probability distribution over the candidate models when an input
instance of a task is given. Both estimator and selector are jointly trained in
a unified learning framework in conjunction with a sampling-based learning
strategy, without additional computation steps. We demonstrate the proposed
approach for several image classification tasks compared to existing approaches
performing model selection or learning multiple tasks. Experimental results
show that our approach gives not only outstanding performance compared to other
competitors but also the versatility to perform instance-wise model selection
for multiple tasks.Comment: ICCV 201
Weighted-Lasso for Structured Network Inference from Time Course Data
We present a weighted-Lasso method to infer the parameters of a first-order
vector auto-regressive model that describes time course expression data
generated by directed gene-to-gene regulation networks. These networks are
assumed to own a prior internal structure of connectivity which drives the
inference method. This prior structure can be either derived from prior
biological knowledge or inferred by the method itself. We illustrate the
performance of this structure-based penalization both on synthetic data and on
two canonical regulatory networks, first yeast cell cycle regulation network by
analyzing Spellman et al's dataset and second E. coli S.O.S. DNA repair network
by analysing U. Alon's lab data
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