3,877 research outputs found

    Bibliometric Mapping of the Computational Intelligence Field

    Get PDF
    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

    Get PDF
    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

    Get PDF
    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

    Get PDF
    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

    Integrating discrete stochastic models with single-cell and single-molecule experiments

    Get PDF
    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

    Get PDF
    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

    Get PDF
    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
    corecore