Results for 'model genetic networks'

987 found
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  1.  40
    Comparing Boolean and Piecewise Affine Differential Models for Genetic Networks.Jean-Luc Gouzé - 2010 - Acta Biotheoretica 58 (2-3):217-232.
    Multi-level discrete models of genetic networks, or the more general piecewise affine differential models, provide qualitative information on the dynamics of the system, based on a small number of parameters (such as synthesis and degradation rates). Boolean models also provide qualitative information, but are based simply on the structure of interconnections. To explore the relationship between the two formalisms, a piecewise affine differential model and a Boolean model are compared, for the carbon starvation response network in (...)
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  2. Mechanistic and topological explanations in medicine: the case of medical genetics and network medicine.Marie Darrason - 2018 - Synthese 195 (1):147-173.
    Medical explanations have often been thought on the model of biological ones and are frequently defined as mechanistic explanations of a biological dysfunction. In this paper, I argue that topological explanations, which have been described in ecology or in cognitive sciences, can also be found in medicine and I discuss the relationships between mechanistic and topological explanations in medicine, through the example of network medicine and medical genetics. Network medicine is a recent discipline that relies on the analysis of (...)
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  3.  23
    Genetic modules and networks for behavior: lessons from Drosophila.Robert R. H. Anholt - 2004 - Bioessays 26 (12):1299-1306.
    Behaviors are quantitative traits determined through actions of multiple genes and subject to genome–environment interactions. Early studies concentrated on analyzing the effects of single genes on behaviors, often generating views of simplified linear genetic pathways. The genome era has generated a profound paradigm shift enabling us to identify all the genes that contribute to expression of a behavioral phenotype, to investigate how they are organized as functional ensembles and to begin to identify polymorphisms that contribute to phenotypic variation and (...)
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  4.  24
    Integration of Multiple Models with Hybrid Artificial Neural Network-Genetic Algorithm for Soil Cation-Exchange Capacity Prediction.Mahmood Shahabi, Mohammad Ali Ghorbani, Sujay Raghavendra Naganna, Sungwon Kim, Sinan Jasim Hadi, Samed Inyurt, Aitazaz Ahsan Farooque & Zaher Mundher Yaseen - 2022 - Complexity 2022:1-15.
    The potential of the soil to hold plant nutrients is governed by the cation-exchange capacity of any soil. Estimating soil CEC aids in conventional soil management practices to replenish the soil solution that supports plant growth. In this study, a multiple model integration scheme supervised with a hybrid genetic algorithm-neural network was developed and employed to predict the accuracy of soil CEC in Tabriz plain, an arid region of Iran. The standalone models and extreme learning machine ) were (...)
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  5. Complex Non-linear Biodynamics in Categories, Higher Dimensional Algebra and Łukasiewicz–Moisil Topos: Transformations of Neuronal, Genetic and Neoplastic Networks.I. C. Baianu, R. Brown, G. Georgescu & J. F. Glazebrook - 2006 - Axiomathes 16 (1):65-122.
    A categorical, higher dimensional algebra and generalized topos framework for Łukasiewicz–Moisil Algebraic–Logic models of non-linear dynamics in complex functional genomes and cell interactomes is proposed. Łukasiewicz–Moisil Algebraic–Logic models of neural, genetic and neoplastic cell networks, as well as signaling pathways in cells are formulated in terms of non-linear dynamic systems with n-state components that allow for the generalization of previous logical models of both genetic activities and neural networks. An algebraic formulation of variable ‘next-state functions’ is (...)
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  6.  30
    Modeling pathways of differentiation in genetic regulatory networks with Boolean networks.Sheldon Dealy, Stuart Kauffman & Joshua Socolar - 2005 - Complexity 11 (1):52-60.
  7.  16
    Optimization of Backpropagation Neural Network under the Adaptive Genetic Algorithm.Junxi Zhang & Shiru Qu - 2021 - Complexity 2021:1-9.
    This study is to explore the optimization of the adaptive genetic algorithm in the backpropagation neural network, so as to expand the application of the BPNN model in nonlinear issues. Traffic flow prediction is undertaken as a research case to analyse the performance of the optimized BPNN. Firstly, the advantages and disadvantages of the BPNN and genetic algorithm are analyzed based on their working principles, and the AGA is improved and optimized. Secondly, the optimized AGA is applied (...)
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  8.  32
    Design of Nonfragile State Estimator for Discrete-Time Genetic Regulatory Networks Subject to Randomly Occurring Uncertainties and Time-Varying Delays.Yanfeng Zhao, Jihong Shen & Dongyan Chen - 2017 - Complexity:1-17.
    We deal with the design problem of nonfragile state estimator for discrete-time genetic regulatory networks with time-varying delays and randomly occurring uncertainties. In particular, the norm-bounded uncertainties enter into the GRNs in random ways in order to reflect the characteristic of the modelling errors, and the so-called randomly occurring uncertainties are characterized by certain mutually independent random variables obeying the Bernoulli distribution. The focus of the paper is on developing a new nonfragile state estimation method to estimate the (...)
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  9. A Hybrid Fuzzy Wavelet Neural Network Model with Self-Adapted Fuzzy c-Means Clustering and Genetic Algorithm for Water Quality Prediction in Rivers.Mingzhi Huang, Hongbin di TianLiu, Chao Zhang, Xiaohui Yi, Jiannan Cai, Jujun Ruan, Tao Zhang, Shaofei Kong & Guangguo Ying - 2018 - Complexity 2018:1-11.
    Water quality prediction is the basis of water environmental planning, evaluation, and management. In this work, a novel intelligent prediction model based on the fuzzy wavelet neural network including the neural network, the fuzzy logic, the wavelet transform, and the genetic algorithm was proposed to simulate the nonlinearity of water quality parameters and water quality predictions. A self-adapted fuzzy c-means clustering was used to determine the number of fuzzy rules. A hybrid learning algorithm based on a genetic (...)
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  10.  36
    Biological models of security for virus propagation in computer networks.Sanjay Goel & Stephen F. S. F. Bush - 2004 - Login, December 29 (6):49--56.
    This aricle discusses the similarity between the propagation of pathogens (viruses and worms) on computer networks and the proliferation of pathogens in cellular organisms (organisms with genetic material contained within a membrane-encased nucleus). It introduces several biological mechanisms which are used in these organisms to protect against such pathogens and presents security models for networked computers inspired by several biological paradigms, including genomics (RNA interference), proteomics (pathway mapping), and physiology (immune system). In addition, the study of epidemiological models (...)
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  11.  52
    Genetic Causation in Complex Regulatory Systems: An Integrative Dynamic Perspective.James DiFrisco & Johannes Jaeger - 2020 - Bioessays 42 (6):1900226.
    The logic of genetic discovery has changed little over time, but the focus of biology is shifting from simple genotype–phenotype relationships to complex metabolic, physiological, developmental, and behavioral traits. In light of this, the traditional reductionist view of individual genes as privileged difference‐making causes of phenotypes is re‐examined. The scope and nature of genetic effects in complex regulatory systems, in which dynamics are driven by regulatory feedback and hierarchical interactions across levels of organization are considered. This review argues (...)
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  12.  21
    Topology optimization of computer communication network based on improved genetic algorithm.Kayhan Zrar Ghafoor, Jilei Zhang, Yuhong Fan & Hua Ai - 2022 - Journal of Intelligent Systems 31 (1):651-659.
    The topology optimization of computer communication network is studied based on improved genetic algorithm, a network optimization design model based on the establishment of network reliability maximization under given cost constraints, and the corresponding improved GA is proposed. In this method, the corresponding computer communication network cost model and computer communication network reliability model are established through a specific project, and the genetic intelligence algorithm is used to solve the cost model and computer communication (...)
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  13.  22
    Genetic circuitry controlling motility behaviors of Myxococcus xanthus.Tâm Mignot & John R. Kirby - 2008 - Bioessays 30 (8):733-743.
    M. xanthus has a complex multicellular lifestyle including swarming, predation and development. These behaviors depend on the ability of the cells to achieve directed motility across solid surfaces. M. xanthus cells have evolved two motility systems including Type‐IV pili that act as grappling hooks and a controversial engine involving mucus secretion and fixed focal adhesion sites. The necessity for cells to coordinate the motility systems and to respond rapidly to environmental cues is reflected by a complex genetic network involving (...)
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  14.  16
    Porosity Characterization of Thermal Barrier Coatings by Ultrasound with Genetic Algorithm Backpropagation Neural Network.Shuxiao Zhang, Gaolong Lv, Shifeng Guo, Yanhui Zhang & Wei Feng - 2021 - Complexity 2021:1-9.
    Porosity is considered as one of the most important indicators for the characterization of the comprehensive performance of thermal barrier coatings. In this study, the ultrasonic technique and the artificial neural network optimized with the genetic algorithm are combined to develop an intelligent method for automatic detection and accurate prediction of TBCs’s porosity. A series of physical models of plasma-sprayed ZrO2 coating are established with a thickness of 288 μm and porosity varying from 5.71% to 26.59%, and the ultrasonic (...)
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  15.  16
    Model Predictive Control of Nonlinear System Based on GA-RBP Neural Network and Improved Gradient Descent Method.Youming Wang & Didi Qing - 2021 - Complexity 2021:1-14.
    A model predictive control method based on recursive backpropagation neural network and genetic algorithm is proposed for a class of nonlinear systems with time delays and uncertainties. In the offline modeling stage, a multistep-ahead predictor with GA-RBP neural network is designed, where GA-BP neural network is used as a one-step prediction model and GA is employed to train the initial weights and bias of the BP neural network. The incorporation of GA into RBP can reduce the possibility (...)
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  16. Are self-organizing biochemical networks emergent?Christophe Malaterre - 2009 - In Maryvonne Gérin & Marie-Christine Maurel, Origins of Life: Self-Organization and/or Biological Evolution? EDP Sciences. pp. 117--123.
    Biochemical networks are often called upon to illustrate emergent properties of living systems. In this contribution, I question such emergentist claims by means of theoretical work on genetic regulatory models and random Boolean networks. If the existence of a critical connectivity Kc of such networks has often been coined “emergent” or “irreducible”, I propose on the contrary that the existence of a critical connectivity Kc is indeed mathematically explainable in network theory. This conclusion also applies to (...)
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  17.  55
    Multiscale Modeling of Gene–Behavior Associations in an Artificial Neural Network Model of Cognitive Development.Michael S. C. Thomas, Neil A. Forrester & Angelica Ronald - 2016 - Cognitive Science 40 (1):51-99.
    In the multidisciplinary field of developmental cognitive neuroscience, statistical associations between levels of description play an increasingly important role. One example of such associations is the observation of correlations between relatively common gene variants and individual differences in behavior. It is perhaps surprising that such associations can be detected despite the remoteness of these levels of description, and the fact that behavior is the outcome of an extended developmental process involving interaction of the whole organism with a variable environment. Given (...)
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  18.  42
    A causal Bayesian network model of disease progression mechanisms in chronic myeloid leukemia.Daniel Koch, Robert Eisinger & Alexander Gebharter - 2017 - Journal of Theoretical Biology 433:94-105.
    Chronic myeloid leukemia (CML) is a cancer of the hematopoietic system initiated by a single genetic mutation which results in the oncogenic fusion protein Bcr-Abl. Untreated, patients pass through different phases of the disease beginning with the rather asymptomatic chronic phase and ultimately culminating into blast crisis, an acute leukemia resembling phase with a very high mortality. Although many processes underlying the chronic phase are well understood, the exact mechanisms of disease progression to blast crisis are not yet revealed. (...)
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  19.  39
    Behaving: What's Genetic, What's Not, and Why Should We Care?Kenneth F. Schaffner - 2016 - New York, US: Oxford University Press USA.
    Behaving presents an overview of the recent history and methodology of behavioral genetics and psychiatric genetics, informed by a philosophical perspective. Kenneth F. Schaffner addresses a wide range of issues, including genetic reductionism and determinism, "free will," and quantitative and molecular genetics. The latter covers newer genome-wide association studies that have produced a paradigm shift in the subject, and generated the problem of "missing heritability." Schaffner also presents cases involving pro and con arguments for genetic testing for IQ (...)
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  20.  89
    Simulation-Based Optimization on the System-of-Systems Model via Model Transformation and Genetic Algorithm: A Case Study of Network-Centric Warfare.Bong Gu Kang, Seon Han Choi, Se Jung Kwon, Jun Hee Lee & Tag Gon Kim - 2018 - Complexity 2018:1-15.
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  21.  11
    Translating at Work: Genetically Modified Mouse Models and Molecularization in the Environmental Health Sciences.Sara Shostak - 2007 - Science, Technology, and Human Values 32 (3):315-338.
    This paper examines processes of translation through which molecular genetic technologies and practices are incorporated into environmental health research and regulation. Specifically, it considers how scientists, risk assessors, and regulators have used genetically modified mouse models to translate across scientific disciplines, articulate emergent molecular forms, standards, and practices with the extant? gold standard,? and establish roles for molecular knowledge in risk assessment and regulation. Noting variation both within and between regulatory agencies in responses to data from these models, the (...)
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  22.  16
    Anthropo-Genetic Algorithm of the Mind.Meric Bilgic - 2024 - Open Journal of Philosophy 14 (1):161-179.
    This study aims to develop a hybrid model to represent the human mind from a functionalist point of view that can be adapted to artificial intelligence. The model is not a realistic theory of the neural network of the brain but an instrumentalist AI model, which means that there can be some other representative models too. It had been thought that the provability of an axiomatic system requires the completeness of a formal system. However, Gödel proved that (...)
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  23.  61
    Waddington’s Unfinished Critique of Neo-Darwinian Genetics: Then and Now.Adam S. Wilkins - 2008 - Biological Theory 3 (3):224-232.
    C.H. Waddington is today remembered chiefly as a Drosophila developmental geneticist who developed the concepts of “canalization” and “the epigenetic landscape.” In his lifetime, however, he was widely perceived primarily as a critic of Neo-Darwinian evolutionary theory. His criticisms of Neo-Darwinian evolutionary theory were focused on what he saw as unrealistic, “atomistic” models of both gene selection and trait evolution. In particular, he felt that the Neo-Darwinians badly neglected the phenomenon of extensive gene interactions and that the “randomness” of mutational (...)
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  24. Genetic susceptibility to a complex disease: the key role of functional redundancy.Gaëlle Debret, Camille Jung, Jean-Pierre Hugot, Leigh Pascoe, Jean-Marc Victor & Annick Lesne - 2011 - History and Philosophy of the Life Sciences 33 (4).
    Complex diseases involve both a genetic component and a response to environmental factors or lifestyle changes. Recently, genome-wide association studies (GWAS) have succeeded in identifying hundreds of polymorphisms that are statistically associated with complex diseases. However, the association is usually weak and none of the associated allelic forms is either necessary or sufficient for the disease occurrence. We argue that this promotes a network view, centred on functional redundancy. We adapted reliability theory to the concerned sub-network, modelled as a (...)
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  25.  12
    A Short-Term Load Forecasting Model of LSTM Neural Network considering Demand Response.Xifeng Guo, Qiannan Zhao, Shoujin Wang, Dan Shan & Wei Gong - 2021 - Complexity 2021:1-7.
    As one of the key technologies for accelerating the construction of the ubiquitous Internet of Things, demand response not only guides users to participate in power market operations but also increases the randomness of grid operations and the difficulty of load forecasting. In order to solve the problem of rough feature engineering processing and low prediction accuracy, a short-term load forecasting model of LSTM neural network considering demand response is proposed. First of all, in view of the strong randomness (...)
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  26.  26
    Constructing Bayesian Network Models of Gene Expression Networks from Microarray Data.Pater Spirtes, Clark Glymour, Richard Scheines, Stuart Kauffman, Valerio Aimale & Frank Wimberly - unknown
    Through their transcript products genes regulate the rates at which an immense variety of transcripts and subsequent proteins occur. Understanding the mechanisms that determine which genes are expressed, and when they are expressed, is one of the keys to genetic manipulation for many purposes, including the development of new treatments for disease. Viewing each gene in a genome as a distinct variable that is either on or off, or more realistically as a continuous variable, the values of some of (...)
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  27.  18
    Normative and Pragmatic Dimensions of Genetic Counseling: Negotiating Genetics and Ethics.Joseph B. Fanning - 2016 - Cham: Springer Verlag.
    This book provides an elaboration and evaluation of the dominant conceptions of genetic counseling as they are accounted for in three different models: the teaching model; the psychotherapeutic model; and the responsibility model. The elaboration of these models involves an identification of the larger traditions, visions and theories of communication that underwrite them; the evaluation entails an assessment of each model’s theses and ultimately a comparison of their adequacy in response to two important concerns in (...)
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  28. Unifying the essential concepts of biological networks: biological insights and philosophical foundations.Daniel Kostic, Claus Hilgetag & Marc Tittgemeyer - 2020 - Philosophical Transactions of the Royal Society B: Biological Sciences 375 (1796):1-8.
    Over the last decades, network-based approaches have become highly popular in diverse fields of biology, including neuroscience, ecology, molecular biology and genetics. While these approaches continue to grow very rapidly, some of their conceptual and methodological aspects still require a programmatic foundation. This challenge particularly concerns the question of whether a generalized account of explanatory, organisational and descriptive levels of networks can be applied universally across biological sciences. To this end, this highly interdisciplinary theme issue focuses on the definition, (...)
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  29.  81
    Enterprise Network Marketing Prediction Using the Optimized GA-BP Neural Network.Rui Wang - 2020 - Complexity 2020:1-9.
    As a brand-new marketing method, network marketing has gradually become one of the main ways and means for enterprises to improve profitability and competitiveness with its unique advantages. Using these marketing data to build a model can dig out useful information that the business is concerned about, and the company can then formulate marketing strategies based on this information. Sales forecasting is to speculate on the future based on historical sales. It is a tool for companies to determine production (...)
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  30.  74
    Unifying the essential concepts of biological networks.Daniel Kostic, Claus Hilgetag & Marc Tittgemeyer (eds.) - 2020 - Oxford, UK: Royal Society.
    Over the last two decades, network-focused approaches have become highly popular in diverse fields of biology, including neuroscience, ecology, molecular biology and genetics. While the network approach continues to grow very rapidly, some of its conceptual and methodological aspects still require a programmatic foundation. This challenge particularly concerns the question of whether a generalized account of explanatory, organisational and descriptive levels of networks can be applied universally across biological sciences. Consequently, the central focus of this theme issue will be (...)
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  31.  34
    Newly evolved genes: Moving from comparative genomics to functional studies in model systems.José M. Ranz & John Parsch - 2012 - Bioessays 34 (6):477-483.
    Genes are gained and lost over the course of evolution. A recent study found that over 1,800 new genes have appeared during primate evolution and that an unexpectedly high proportion of these genes are expressed in the human brain. But what are the molecular functions of newly evolved genes and what is their impact on an organism's fitness? The acquisition of new genes may provide a rich source of genetic diversity that fuels evolutionary innovation. Although gene manipulation experiments are (...)
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  32.  44
    Predicting phenotypic effects of gene perturbations in C. elegans using an integrated network model.Karsten Borgwardt - 2008 - Bioessays 30 (8):707-710.
    Predicting the phenotype of an organism from its genotype is a central question in genetics. Most importantly, we would like to find out if the perturbation of a single gene may be the cause of a disease. However, our current ability to predict the phenotypic effects of perturbations of individual genes is limited. Network models of genes are one tool for tackling this problem. In a recent study, (Lee et al.) it has been shown that network models covering the majority (...)
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  33.  16
    Emergency Scheduling Optimization Simulation of Cloud Computing Platform Network Public Resources.Dingrong Liu, Zhigang Yao & Liukui Chen - 2021 - Complexity 2021:1-11.
    Emergency scheduling of public resources on the cloud computing platform network can effectively improve the network emergency rescue capability of the cloud computing platform. To schedule the network common resources, it is necessary to generate the initial population through the Hamming distance constraint and improve the objective function as the fitness function to complete the emergency scheduling of the network common resources. The traditional method, from the perspective of public resource fairness and priority mapping, uses incremental optimization algorithm to realize (...)
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  34.  34
    The Aims and Structures of Research Projects That Use Gene Regulatory Information with Evolutionary Genetic Models.Steve Elliott - 2017 - Dissertation, Arizona State University
    At the interface of developmental biology and evolutionary biology, the very criteria of scientific knowledge are up for grabs. A central issue is the status of evolutionary genetics models, which some argue cannot coherently be used with complex gene regulatory network (GRN) models to explain the same evolutionary phenomena. Despite those claims, many researchers use evolutionary genetics models jointly with GRN models to study evolutionary phenomena. This dissertation compares two recent research projects in which researchers jointly use the two kinds (...)
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  35.  76
    Conceptual and methodological biases in network models.Ehud Lamm - 2009 - Annals of the New York Academy of Sciences 1178:291-304.
    Many natural and biological phenomena can be depicted as networks. Theoretical and empirical analyses of networks have become prevalent. I discuss theoretical biases involved in the delineation of biological networks. The network perspective is shown to dissolve the distinction between regulatory architecture and regulatory state, consistent with the theoretical impossibility of distinguishing a priori between “program” and “data”. The evolutionary significance of the dynamics of trans-generational and inter-organism regulatory networks is explored and implications are presented for (...)
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  36.  20
    The Governance of Genetic Information: Who Decides?Heather Widdows & Caroline Mullen (eds.) - 2009 - Cambridge University Press.
    This volume maps the areas of ethical concern in the debate regarding the governance of genetic information, and suggests alternative ethical frameworks and models of regulation in order to inform its restructuring. Genetic governance is at the heart of medical and scientific developments, and is connected to global exploitation, issues of commodification, commercialisation and ownership, the concepts of property and intellectual property and concerns about individual and communal identity. Thus the decisions that are made in the next few (...)
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  37. Tuning to the significant: neural and genetic processes underlying affective enhancement of visual perception and memory.Jelena Markovic, Adam K. Anderson & Rebecca M. Todd - 2014 - Behavioural Brain Research 1 (259):229-241.
    Emotionally arousing events reach awareness more easily and evoke greater visual cortex activation than more mundane events. Recent studies have shown that they are also perceived more vividly and that emotionally enhanced perceptual vividness predicts memory vividness. We propose that affect-biased attention (ABA) – selective attention to emotionally salient events – is an endogenous attentional system tuned by an individual's history of reward and punishment. We present the Biased Attention via Norepinephrine (BANE) model, which unifies genetic, neuromodulatory, neural (...)
     
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  38.  49
    Can Darwinian Mechanisms Make Novel Discoveries?: Learning from discoveries made by evolving neural networks.Robert T. Pennock - 2000 - Foundations of Science 5 (2):225-238.
    Some philosophers suggest that the development of scientificknowledge is a kind of Darwinian process. The process of discovery,however, is one problematic element of this analogy. I compare HerbertSimon's attempt to simulate scientific discovery in a computer programto recent connectionist models that were not designed for that purpose,but which provide useful cases to help evaluate this aspect of theanalogy. In contrast to the classic A.I. approach Simon used, ``neuralnetworks'' contain no explicit protocols, but are generic learningsystems built on the model (...)
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  39. Information Dynamics across Linked Sub-Networks: Germs, Genes, and Memes.Patrick Grim, Daniel J. Singer, Christopher Reade & Stephen Fisher - 2011 - In Patrick Grim, Daniel J. Singer, Christopher Reade & Stephen Fisher, Proceedings, AAAI Fall Symposium on Complex Adaptive Systems: Energy, Information and Intelligence. AAAI Press.
    Beyond belief change and meme adoption, both genetics and infection have been spoken of in terms of information transfer. What we examine here, concentrating on the specific case of transfer between sub-networks, are the differences in network dynamics in these cases: the different network dynamics of germs, genes, and memes. Germs and memes, it turns out, exhibit a very different dynamics across networks. For infection, measured in terms of time to total infection, it is network type rather than (...)
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  40.  70
    Embodied anomaly resolution in molecular genetics: A case study of RNAi.John J. Sung - 2008 - Foundations of Science 13 (2):177-193.
    Scientific anomalies are observations and facts that contradict current scientific theories and they are instrumental in scientific theory change. Philosophers of science have approached scientific theory change from different perspectives as Darden (Theory change in science: Strategies from Mendelian genetics, 1991) observes: Lakatos (In: Lakatos, Musgrave (eds) Criticism and the growth of knowledge, 1970) approaches it as a progressive “research programmes” consisting of incremental improvements (“monster barring” in Lakatos, Proofs and refutations: The logic of mathematical discovery, 1976), Kuhn (The structure (...)
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  41.  28
    Determination of Fire Resistance of Eccentrically Loaded Reinforced Concrete Columns Using Fuzzy Neural Networks.Marijana Lazarevska, Ana Trombeva Gavriloska, Mirjana Laban, Milos Knezevic & Meri Cvetkovska - 2018 - Complexity 2018:1-12.
    Artificial neural networks, in interaction with fuzzy logic, genetic algorithms, and fuzzy neural networks, represent an example of a modern interdisciplinary field, especially when it comes to solving certain types of engineering problems that could not be solved using traditional modeling methods and statistical methods. They represent a modern trend in practical developments within the prognostic modeling field and, with acceptable limitations, enjoy a generally recognized perspective for application in construction. Results obtained from numerical analysis, which includes (...)
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  42. Environmental Variability and the Emergence of Meaning: Simulational Studies across Imitation, Genetic Algorithms, and Neural Nets.Patrick Grim - 2006 - In Angelo Loula, Ricardo Gudwin & Jo?O. Queiroz, Artificial Cognition Systems. Idea Group Publishers. pp. 284-326.
    A crucial question for artificial cognition systems is what meaning is and how it arises. In pursuit of that question, this paper extends earlier work in which we show that emergence of simple signaling in biologically inspired models using arrays of locally interactive agents. Communities of "communicators" develop in an environment of wandering food sources and predators using any of a variety of mechanisms: imitation of successful neighbors, localized genetic algorithms and partial neural net training on successful neighbors. Here (...)
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  43.  7
    (1 other version)Setting the Scientific Bar for the Genetics of Behavior.Eric Turkheimer & Sarah Rodock Greer - 2024 - Philosophy Psychiatry and Psychology 31 (4):455-460.
    In lieu of an abstract, here is a brief excerpt of the content:Setting the Scientific Bar for the Genetics of BehaviorEric Turkheimer, PhD (bio) and Sarah Rodock Greer, BA (bio)We are grateful for the opportunity to respond to such a varied and challenging set of commentaries. They range from highly supportive to quite disputatious; we will repay the supportive ones ironically, by discussing them only briefly. That will allow us to expand a bit on the more difficult comments, and of (...)
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  44.  29
    Intelligent Ensemble Deep Learning System for Blood Glucose Prediction Using Genetic Algorithms.Dae-Yeon Kim, Dong-Sik Choi, Ah Reum Kang, Jiyoung Woo, Yechan Han, Sung Wan Chun & Jaeyun Kim - 2022 - Complexity 2022:1-10.
    Forecasting blood glucose values for patients can help prevent hypoglycemia and hyperglycemia events in advance. To this end, this study proposes an intelligent ensemble deep learning system to predict BG values in 15, 30, and 60 min prediction horizons based on historical BG values collected via continuous glucose monitoring devices as an endogenous factor and carbohydrate intake and insulin administration information as exogenous factors. Although there are numerous deep learning algorithms available, this study applied five algorithms, namely, recurrent neural network, (...)
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  45.  34
    Global Stability of Reversible Enzymatic Metabolic Chains.Ibrahima Ndiaye & Jean-Luc Gouzé - 2013 - Acta Biotheoretica 61 (1):41-57.
    We consider metabolic networks with reversible enzymatic reactions. The model is written as a system of ordinary differential equations, possibly with inputs and outputs. We prove the global stability of the equilibrium , using techniques of monotone systems and compartmental matrices. We show that the equilibrium does not always exist. Finally, we consider a metabolic system coupled with a genetic network, and we study the dependence of the metabolic equilibrium with respect to concentrations of enzymes. We give (...)
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  46.  11
    Combination Forecast of Economic Chaos Based on Improved Genetic Algorithm.Yankun Yang - 2021 - Complexity 2021:1-11.
    The deterministic economic system will also produce chaotic dynamic behaviour, so economic chaos is getting more and more attention, and the research of economic chaos forecasting methods has become an important topic at present. The traditional economic chaos forecasting models are mostly based on large samples, but in actual production activities, there are a large number of small-sample economic chaos problems, and there is still no effective solution. This paper proposes a combined forecasting model based on the traditional economic (...)
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  47.  10
    A Method of Partner Selection for Knowledge Collaboration Teams using Weighted Social Network Analysis.Jiafu Su, Yu Yang, Kunpeng Yu & Na Zhang - 2018 - Journal of Intelligent Systems 27 (4):577-591.
    Partner selection is the primary aspect of the formation of knowledge collaboration teams. We propose a method of partner selection for KCTs based on a weighted social network analysis method in which the individual knowledge competence and the collaboration performance of candidates are both considered. To select the desired partners, a biobjective 0-1 model is built, integrating the knowledge competence and collaboration performance, which is an NP-hard problem. Then, a multiobjective genetic algorithm is developed to solve the proposed (...)
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  48.  67
    Complex realities require complex theories: Refining and extending the network approach to mental disorders.Angélique Oj Cramer, Lourens J. Waldorp, Han Lj van der Maas & Denny Borsboom - 2010 - Behavioral and Brain Sciences 33 (2-3):178-193.
    The majority of commentators agree on one thing: Our network approach might be the prime candidate for offering a new perspective on the origins of mental disorders. In our response, we elaborate on refinements (e.g., cognitive and genetic levels) and extensions (e.g., to Axis II disorders) of the network model, as well as discuss ways to test its validity.
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    Claim Amount Forecasting and Pricing of Automobile Insurance Based on the BP Neural Network.Wenguang Yu, Guofeng Guan, Jingchao Li, Qi Wang, Xiaohan Xie, Yu Zhang, Yujuan Huang, Xinliang Yu & Chaoran Cui - 2021 - Complexity 2021:1-17.
    The BP neural network model is a hot issue in recent academic research, and it has been successfully applied to many other fields, but few researchers apply the BP neural network model to the field of automobile insurance. The main method that has been used in the prediction of the total claim amount in automobile insurance is the generalized linear model, where the BP neural network model could provide a different approach to estimate the total claim (...)
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    Psychic systems and metaphysical machines: experiencing behavioural prediction with neural networks.Max B. Kazemzadeh - 2010 - Technoetic Arts 8 (2):189-198.
    We are living in a time of meta-organics and post-biology, where we perceive everything in our world as customizable and changeable. Modelling biology within a technological context allows us to investigate GEO-volutionary alternatives/alterations to our original natural systems, where augmentation and transmutation become standards in search of overall betterment (Genetically Engineered Organics). Our expectations for technology exceeds ubiquitous access and functional perfection and enters the world of technoetics, where our present hyper-functional, immersively multi-apped, borderline-prosthetic, global village devices fail to satiate (...)
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