3,877 research outputs found
Bibliometric Mapping of the Computational Intelligence Field
In this paper, a bibliometric study of the computational intelligence field is presented. Bibliometric maps showing the associations between the main concepts in the field are provided for the periods 1996–2000 and 2001–2005. Both the current structure of the field and the evolution of the field over the last decade are analyzed. In addition, a number of emerging areas in the field are identified. It turns out that computational intelligence can best be seen as a field that is structured around four important types of problems, namely control problems, classification problems, regression problems, and optimization problems. Within the computational intelligence field, the neural networks and fuzzy systems subfields are fairly intertwined, whereas the evolutionary computation subfield has a relatively independent position.neural networks;bibliometric mapping;fuzzy systems;bibliometrics;computational intelligence;evolutionary computation
Information visualization for DNA microarray data analysis: A critical review
Graphical representation may provide effective means of making sense of the complexity and sheer volume of data produced by DNA microarray experiments that monitor the expression patterns of thousands of genes simultaneously. The ability to use ldquoabstractrdquo graphical representation to draw attention to areas of interest, and more in-depth visualizations to answer focused questions, would enable biologists to move from a large amount of data to particular records they are interested in, and therefore, gain deeper insights in understanding the microarray experiment results. This paper starts by providing some background knowledge of microarray experiments, and then, explains how graphical representation can be applied in general to this problem domain, followed by exploring the role of visualization in gene expression data analysis. Having set the problem scene, the paper then examines various multivariate data visualization techniques that have been applied to microarray data analysis. These techniques are critically reviewed so that the strengths and weaknesses of each technique can be tabulated. Finally, several key problem areas as well as possible solutions to them are discussed as being a source for future work
Cell states along oligodendrocyte development and disease
The brain, one of the most complex organs in the body, where an immense diversity of cell
states emerges from simple structure, where function arises from sets of regulatory principles
and pattern persist where individual cells do not. Revealing the regulatory underpinnings of
the brain, from unspecified cell states to diversity, is paramount for achieving a thorough
understanding of the development process and generating insight into the disease states of the
brain. This thesis is an exploration into how canonical regulatory factors and elements, such
as transcription factors and genes, lock a regulatory system in a multi-outcome network with
limited possible states.
The work in this thesis focuses on the oligodendrocyte lineage, a glial cell known for it’s
supportive role in the central nervous system, where it facilitates electrical transmission
through the enscheathment of axons. Oligodendrocytes (OLs) lie at the heart of multiple
sclerosis (MS), a disease where an immune response is mounted against myelin. As a
response, oligodendrocyte precursor cells (OPCs) move towards lesions and remyelinate
axons, however, this mechanism fails in later stages of the disease. Thus, an understanding to
how OPCs develop is vital to amelioration of the altered oligodendrocyte population.
In Paper I we reveal a previously underestimated heterogeneity within the oligodendrocyte
lineage in mouse. We show that OL maturation is an ongoing process, albeit, decreasing in
frequency with age. Furthermore, complex wheel training in mice revealed that the OLs
respond to this challenge through an increase in differentiation.
Paper II investigates the cellular response in the experimental autoimmune encephalomyelitis
(EAE) disease mouse model of MS, where we find a tailored response by the
resident OL population, changed from its normal transcriptional program, expressing a
spectrum of genes related to survival, immunological stimulation, phagocytosis, and active
differentiation. Furthermore, we provide evidence that OLs can elicit responses from T cells.
In Paper III we explore the different waves of OPC generation in the developing mouse
brain at embryonic day 13.5 and postnatal day 7. We show that recently Pdgfra expressing
cells at the E13.5 time point exhibit a multitude of patterning genes, and we show the
emergence of a possible OPC progenitor through the inclusion of a bridging E17.5 time point
population. This pre-OPC population is biased towards expressing glial and OL lineage
specifying genes such as Olig1, Olig2, Ptprz1, and Bcan. Furthermore, lineage tracing of
OPC developmental waves, shows no transcriptional differences, leading us to conclude that
OPCs are generally naïve to the time or region of specification.
In Paper IV we show that we are able to detect OPC formation in the developing human
forebrain. We detect OPCs at the earliest sampled time point post conception week 8. We
attempt to recover the path of OPC formation, and investigate the regulatory dynamics in the
specification of OPCs
Tiling solutions for optimal biological sensing
Biological systems, from cells to organisms, must respond to the ever
changing environment in order to survive and function. This is not a simple
task given the often random nature of the signals they receive, as well as the
intrinsically stochastic, many body and often self-organized nature of the
processes that control their sensing and response and limited resources.
Despite a wide range of scales and functions that can be observed in the living
world, some common principles that govern the behavior of biological systems
emerge. Here I review two examples of very different biological problems:
information transmission in gene regulatory networks and diversity of adaptive
immune receptor repertoires that protect us from pathogens. I discuss the
trade-offs that physical laws impose on these systems and show that the optimal
designs of both immune repertoires and gene regulatory networks display similar
discrete tiling structures. These solutions rely on locally non-overlapping
placements of the responding elements (genes and receptors) that, overall,
cover space nearly uniformly.Comment: 11 page
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Inference of gene regulatory networks from genome-wide knockout fitness data
Motivation: Genome-wide fitness is an emerging type of high-throughput biological data generated for individual organisms by creating libraries of knockouts, subjecting them to broad ranges of environmental conditions, and measuring the resulting clone-specific fitnesses. Since fitness is an organism-scale measure of gene regulatory network behaviour, it may offer certain advantages when insights into such phenotypical and functional features are of primary interest over individual gene expression. Previous works have shown that genome-wide fitness data can be used to uncover novel gene regulatory interactions, when compared with results of more conventional gene expression analysis. Yet, to date, few algorithms have been proposed for systematically using genome-wide mutant fitness data for gene regulatory network inference. Results: In this article, we describe a model and propose an inference algorithm for using fitness data from knockout libraries to identify underlying gene regulatory networks. Unlike most prior methods, the presented approach captures not only structural, but also dynamical and non-linear nature of biomolecular systems involved. A state–space model with non-linear basis is used for dynamically describing gene regulatory networks. Network structure is then elucidated by estimating unknown model parameters. Unscented Kalman filter is used to cope with the non-linearities introduced in the model, which also enables the algorithm to run in on-line mode for practical use. Here, we demonstrate that the algorithm provides satisfying results for both synthetic data as well as empirical measurements of GAL network in yeast Saccharomyces cerevisiae and TyrR–LiuR network in bacteria Shewanella oneidensis
Integrating discrete stochastic models with single-cell and single-molecule experiments
2019 Summer.Includes bibliographical references.Modern biological experiments can capture the behaviors of single biomolecules within single cells. Much like Robert Brown looking at pollen grains in water, experimentalists have noticed that individual cells that are genetically identical behave seemingly randomly in the way they carry out their most basic functions. The field of stochastic single-cell biology has been focused developing mathematical and computational tools to understand how cells try to buffer or even make use of such fluctuations, and the technologies to measure such fluctuations has vastly improved in recent years. This dissertation is focused on developing new methods to analyze modern single-cell and single-molecule biological data with discrete stochastic models of the underlying processes, such as stochastic gene expression and single-mRNA translation. The methods developed here emphasize a strong link between model and experiment to help understand, design, and eventually control biological systems at the single-cell level
Modelling transcriptional regulation with Gaussian processes
A challenging problem in systems biology is the quantitative modelling
of transcriptional regulation. Transcription factors (TFs), which are the
key proteins at the centre of the regulatory processes, may be subject
to post-translational modification, rendering them unobservable at the
mRNA level, or they may be controlled outside of the subsystem being
modelled. In both cases, a mechanistic model description of the regula-
tory system needs to be able to deal with latent activity profiles of the key
regulators. A promising approach to deal with these difficulties is based
on using Gaussian processes to define a prior distribution over the latent
TF activity profiles. Inference is based on the principles of non-parametric
Bayesian statistics, consistently inferring the posterior distribution of the
unknown TF activities from the observed expression levels of potential
target genes. The present work provides explicit solutions to the differ-
ential equations needed to model the data in this manner, as well as the
derivatives needed for effective optimisation. The work further explores
identifiability issues not fully shown in previous work and looks at how
this can cause difficulties with inference. We subsequently look at how the
method works on two different TFs, including looking at how the model
works with a more biologically realistic mechanistic model. Finally we
analyse the effect of more biologically realistic non-Gaussian noise on the
biologically realistic model showing how this can cause a reduction in the
accuracy of the inference
ODE parameter inference using adaptive gradient matching with Gaussian processes
Parameter inference in mechanistic models based on systems of coupled differential equa- tions is a topical yet computationally chal- lenging problem, due to the need to fol- low each parameter adaptation with a nu- merical integration of the differential equa- tions. Techniques based on gradient match- ing, which aim to minimize the discrepancy between the slope of a data interpolant and the derivatives predicted from the differen- tial equations, offer a computationally ap- pealing shortcut to the inference problem. The present paper discusses a method based on nonparametric Bayesian statistics with Gaussian processes due to Calderhead et al. (2008), and shows how inference in this model can be substantially improved by consistently inferring all parameters from the joint dis- tribution. We demonstrate the efficiency of our adaptive gradient matching technique on three benchmark systems, and perform a de- tailed comparison with the method in Calder- head et al. (2008) and the explicit ODE inte- gration approach, both in terms of parameter inference accuracy and in terms of computa- tional efficiency
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