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Robust identification of large genetic networks

D Di Bernardo, T S Gardner, J J Collins
2004 Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing  
Our novel approach is experimentally feasible and it is readily applicable to large genetic networks.  ...  We developed a novel algorithm to identify a large genetic network, as a set of linear differential equations, starting from measurements of gene expression at steady state following transcriptional perturbations  ... 
pmid:14992527 fatcat:knk7q3v2uvdrrnbfajs4grpg2m

Identification of stable genetic networks using convex programming

Michael M. Zavlanos, A. Agung Julius, Stephen P. Boyd, George J. Pappas
2008 2008 American Control Conference  
Our method is based on a convex programming relaxation for handling the sparsity constraint, and therefore is applicable to the identification of genome-scale genetic networks.  ...  Our identification algorithm can also incorporate a variety of possible prior knowledge of the network structure, which can be either qualitative, specifying positive, negative or no interactions between  ...  gene interactions in large genetic networks.  ... 
doi:10.1109/acc.2008.4586910 dblp:conf/amcc/ZavlanosJBP08 fatcat:i3ikubwf6vc45jnzfj57gr2u6y

Perspectives on systems biology

Hiroaki Kitano
2000 New generation computing  
Possible Genetic Regulatory Network for Stripe Patterns 4.  ...  System Structure Identification• Approaches for Structure Identification:3) Hybrid Approach -It is likely that some knowledge should be assumed in the process of gene network inference.  ... 
doi:10.1007/bf03037529 fatcat:o7urcr7msvbh3fg6in42uxvdjm

Robust radial basis function neural networks

Chien-Cheng Lee, Pau-Choo Chung, Jea-Rong Tsai, Chein-I Chang
1999 IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)  
In this paper, a novel method for robust nonlinear system identification is constructed to overcome the problems of traditional RBFNNs.  ...  Using Genetic Algorithm (GA) for training SVR and select the best parameters as an initialization of RBFNNs.  ...  Robust identification is a method to determine the parameters of neural network when training data contaminated with noise and outliers.  ... 
doi:10.1109/3477.809023 pmid:18252348 fatcat:fnpekr7ekzfnjbd7sc6lc62clq

Leveraging gene co-expression networks to pinpoint the regulation of complex traits and disease, with a focus on cardiovascular traits

M. Rotival, E. Petretto
2013 Briefings in Functional Genomics  
Finally, we discuss specific applications of network-based approaches to the study of cardiovascular traits, which highlight the power of integrated analyses of networks, genetic and gene-regulation data  ...  of gene co-expression networks.  ...  , also referred to as 'master genetic regulators' of the networks.  ... 
doi:10.1093/bfgp/elt030 pmid:23960099 fatcat:nrnor6xzfvahrhkmtsegog7vlu

Lessons from Model Organisms: Phenotypic Robustness and Missing Heritability in Complex Disease

Christine Queitsch, Keisha D. Carlson, Santhosh Girirajan, Susan M. Rosenberg
2012 PLoS Genetics  
We propose feasible approaches to measure robustness in large human populations, proof-of-principle experiments for robustness markers in model organisms, and a new GWAS design that takes differences in  ...  In diverse model organisms, phenotypic robustness differs among individuals, and those with decreased robustness show increased penetrance of mutations and express previously cryptic genetic variation.  ...  The number of network hubs in humans is large but not infinitely large.  ... 
doi:10.1371/journal.pgen.1003041 pmid:23166511 pmcid:PMC3499356 fatcat:7pdwr2vl3fb2pbjwlo67wm7zwa

Robustness: mechanisms and consequences

Joanna Masel, Mark L. Siegal
2009 Trends in Genetics  
The identification and characterization of phenotypic capacitors -which act as switches of the degree of robustness -are critical to understanding the mechanisms and consequences of robustness.  ...  Whatever its mechanism or origin, robustness to mutation results in the accumulation of phenotypically cryptic genetic variation.  ...  JM was supported by the National Institutes of Health (R01 GM076041). J.M. is a Pew Scholar in the Biomedical Sciences. MLS was supported by the National Science Foundation (IOS-0642999).  ... 
doi:10.1016/j.tig.2009.07.005 pmid:19717203 pmcid:PMC2770586 fatcat:53x4uhdu65cdjagjkm4jr6t5va

Research on Robustness of Complex Networks Based on Genetic Algorithm

Yi-fan PENG, Xiao-yu ZHANG, Ying SUN
2018 DEStech Transactions on Computer Science and Engineering  
Based on the analysis on influence of network node malfunction to the overall connectivity, we propose a node ranking strategy to optimize the robustness of complex network.  ...  This node ranking strategy is based on the Genetic Algorithm, which is applied to an unweighted graph in this paper, where the robustness value is an optimization target.  ...  make the results of identification and warning of key nodes in complex network more reliable and stable.  ... 
doi:10.12783/dtcse/cmee2017/19994 fatcat:twoezkobafdhhodz2s7sblt42m

Decoupling Environment-Dependent and Independent Genetic Robustness across Bacterial Species

Shiri Freilich, Anat Kreimer, Elhanan Borenstein, Uri Gophna, Roded Sharan, Eytan Ruppin, Herbert M. Sauro
2010 PLoS Computational Biology  
Here we conduct the first large-scale computational study charting the level of robustness of metabolic networks of hundreds of bacterial species across many simulated growth environments.  ...  The evolutionary origins of genetic robustness are still under debate: it may arise as a consequence of requirements imposed by varying environmental conditions, due to intrinsic factors such as metabolic  ...  Our findings thus provide direct large-scale evidence that genetic robustness is associated with environmental robustness.  ... 
doi:10.1371/journal.pcbi.1000690 pmid:20195496 pmcid:PMC2829043 fatcat:7fbvtzlnqnhm5fow4kl7qpulki

A fuzzy-genetic approach to network intrusion detection

Terrence P. Fries
2008 Proceedings of the 2008 GECCO conference companion on Genetic and evolutionary computation - GECCO '08  
Genetic-based systems offer to ability to adapt to changing environments, robustness to noise and the ability to identify unknown attack methods.  ...  In addition, the method demonstrates improved robustness in comparison to other GA-based techniques.  ...  New training data must be classified manually which is not practical for very large volume of network data.  ... 
doi:10.1145/1388969.1389037 dblp:conf/gecco/Fries08 fatcat:eordhnyfrvbxvnsyh2nkqmcjq4

Study on Character Recognition Arithmetic Based on Intelligent Information Processing Technologies

Yingyong Zou, Yongde Zhang, Xin Wang, Guangbin Yu
2014 International Journal of Signal Processing, Image Processing and Pattern Recognition  
Combined neural network and genetic algorithm is to make full use of the advantages of both, so that the new algorithms both neural network learning capability and robustness, but also a strong genetic  ...  Aiming at the complexity and limitations of traditional character recognition design method, an algorithm combined with genetic algorithms and neural network is proposed.  ...  To combine neural networks and genetic algorithms, you can make full use of the advantages of both, so that the learning ability and robustness of the new algorithm is a neural network and genetic algorithm  ... 
doi:10.14257/ijsip.2014.7.3.03 fatcat:nqsbd6ixcvefrkudcq2yjikoee

An integer optimization algorithm for robust identification of non-linear gene regulatory networks

Nishanth Chemmangattuvalappil, Keith Task, Ipsita Banerjee
2012 BMC Systems Biology  
The basic network identification formulation exploits the trait of sparsity of biological interactions.  ...  Results: We have developed a network identification algorithm to accurately infer both the topology and strength of regulatory interactions from time series gene expression data in the presence of significant  ...  In the presence of large experimental repeats it may be possible to determine robustness of identified network by repeatedly solving the network identification problem at each of the experimental data  ... 
doi:10.1186/1752-0509-6-119 pmid:22937832 pmcid:PMC3444924 fatcat:raz2vrtiyzejlicofug5bj2tny

Protein Complex, Gene, and Regulatory Modules in Cancer Heterogeneity

Nikolaos A. Papanikolaou, Athanasios G. Papavassiliou
2008 Molecular Medicine  
tumors with heterogeneity and robustness, and largely determine disease progression.  ...  In this commentary, we discuss sources of human solid tumor heterogeneity, robustness, and their dependence on the existence of differently reorganized complexes and networks, using the example of epidermal  ... 
doi:10.2119/2008-00083.papanikolaou pmid:18654660 pmcid:PMC2480613 fatcat:z2jqehmhbnfqhmwk26fl3gtpk4

Genetic Screening for Signal Transduction in the Era of Network Biology

Adam Friedman, Norbert Perrimon
2007 Cell  
In contrast to animal-based mutant phenotype assays, recent biochemical and quantitative genetic studies have identified hundreds of potential regulators of known signaling pathways.  ...  between previous models and new data, put forward a different signaling conceptual framework incorporating time-dependent quantitative contributions, and suggest how this new framework can impact our study of  ...  N.P. is an investigator of the Howard Hughes Medical Institute.  ... 
doi:10.1016/j.cell.2007.01.007 pmid:17254958 fatcat:od36dyirrje2lhftanmclc4nya

Computational systems biology

Hiroaki Kitano
2002 Nature  
of the network and the specific elements involved.  ...  A popular notion of complex systems is of very large numbers of simple and identical elements interacting to produce 'complex' behaviours. The reality of biological systems is somewhat different.  ...  Comparative studies on robustness of large-scale networks show that scale-free networks (also known as 'small world' or Erdös-Rényi networks) are more robust than randomly connected networks against random  ... 
doi:10.1038/nature01254 pmid:12432404 fatcat:angnkd6ys5fqpk3a4h6pwytkxa
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