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Multiobjective Optimization in Bioinformatics and Computational Biology

Julia Handl, Douglas B. Kell, Joshua Knowles
2007 IEEE/ACM Transactions on Computational Biology & Bioinformatics  
An original contribution of the review is the identification of five distinct "contexts," giving rise to multiple objectives: These are used to explain the reasons behind the use of multiobjective optimization  ...  This paper reviews the application of multiobjective optimization in the fields of bioinformatics and computational biology.  ...  using multiobjective optimization.  ... 
doi:10.1109/tcbb.2007.070203 pmid:17473320 fatcat:phhnntgxwbad7mdpcl2fjjx4oe

The Riddle of Togelby [article]

Daniel Ashlock, Christoph Salge
2019 arXiv   pre-print
In [32] multiobjective optimization is applied to the task of search-based procedural content generation for real time strategy maps. III.  ...  Ashlock and Tsang [11] produced evolved art using 1-dimensional CA rules. CA rules were evolved using a string representation. The CA either underwent slow persistent growth, or planned senescence.  ... 
arXiv:1906.03997v1 fatcat:n3oqpup5uff6bkmmuof65ttnry

Complexity, Biocomplexity, the Connectionist Conjecture and Ontology of Complexity

Debaprasad Mukherjee
2009 Social Science Research Network  
Example of biological complexity is illustrated by the fact that levels of biological complexity extend beyond the intricacies of the genome and protein structures through supramolecular complexes and  ...  The paper also assumes that signaling and communication within the living world and of the living world with the environment creates the connectionist structure of the biocomplexity.  ...  Several established data bases and data retrieval systems must be put to use all together in an integrated framework and linear and nonlinear (clustering) data mining protocols to be used to extract the  ... 
doi:10.2139/ssrn.3075414 fatcat:onafkzc2mnbsliqmko6iazfz6e

Predictive analytics of environmental adaptability in multi-omic network models

Claudio Angione, Pietro Lió
2015 Scientific Reports  
After the integration of multiple omics into the model, we propose a multiobjective optimization algorithm to find the allowable and optimal metabolic phenotypes through concurrent maximization or minimization  ...  For instance, a highly adaptive bacterium ensures that the structure of its metabolism and the pathway productivity rapidly evolve over time due to varying environmental conditions or selective pressure  ...  Methods Multiobjective optimization.  ... 
doi:10.1038/srep15147 pmid:26482106 pmcid:PMC4611489 fatcat:outhifas4recpl5ruxc6ujiezy

TriRNSC: triclustering of gene expression microarray data using restricted neighbourhood search

Bhawani Sankar Biswal, Sabyasachi Patra, Anjali Mohapatra, Swati Vipsita
2020 IET Systems Biology  
TriRNSC is based on restricted neighbourhood search clustering (RNSC), a popular graph-based clustering approach considering the genes, the experimental conditions and the time points at an instance.  ...  Gene Ontology and KEGG pathway analysis are used to validate the TriRNSC results biologically.  ...  Gene expression profiles represented in a cluster-based form are now being used in many kinds of analysis.  ... 
doi:10.1049/iet-syb.2020.0024 pmid:33399096 pmcid:PMC8687346 fatcat:4wy6ueig35e3bbky7zd4au723m

Welcome message from the General Chairs

Giovanni Giambene, Boon Sain Yeo
2009 2009 International Workshop on Satellite and Space Communications  
All accepted papers will be included in the Proceeding of Adaptation, Learning and Optimization Series published by Springer-Verlag.  ...  Based on these rigorous reviews, IES 2014 accepted 106 papers for inclusion in the conference program, which represents an acceptance rate of 69%.  ...  His research interests include evolutionary multiobjective optimization, fuzzy genetics-based classifier design and evolutionary games.  ... 
doi:10.1109/iwssc.2009.5286448 fatcat:wcu4uzasizhzjmdkzyekynnqwi

2021 Index IEEE Transactions on Cybernetics Vol. 51

2021 IEEE Transactions on Cybernetics  
Song, J., +, TCYB Sept. 2021 4581-4590 Correntropy-Based Multiview Subspace Clustering.  ...  ., +, TCYB Jan. 2021 346-358 Learning Manifold Structures With Subspace Segmentations.  ... 
doi:10.1109/tcyb.2021.3139447 fatcat:myjx3olwvfcfpgnwvbuujwzyoi

Optimization of amino acid replacement costs by mutational pressure in bacterial genomes

Paweł Błażej, Dorota Mackiewicz, Małgorzata Grabińska, Małgorzata Wnętrzak, Paweł Mackiewicz
2017 Scientific Reports  
Evolutionary Multiobjective Optimization (EMO) approach is used in many optimization problems due to its simplicity and flexibility.  ...  Here, we used a modified version of the Strength Pareto Evolutionary Algorithm (SPEA2) 111 , which is an efficient technique used in many multiobjective optimization problems.  ...  If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly  ... 
doi:10.1038/s41598-017-01130-7 pmid:28432324 pmcid:PMC5430830 fatcat:sdnb5ixndzefjgh7mzni7zjlxe

Evolutionary Computation and Its Applications in Neural and Fuzzy Systems

Biaobiao Zhang, Yue Wu, Jiabin Lu, K.-L. Du
2011 Applied Computational Intelligence and Soft Computing  
Structural optimization is a discrete optimization problem which is very hard to solve using conventional optimization techniques.  ...  Adapting a neural or fuzzy system requires to solve two optimization problems: structural optimization and parametric optimization.  ...  Structural optimization is the first step that tries to find an optimum system structure; it is a discrete (combinatorial) optimization problem and is very hard to solve using conventional calculus-based  ... 
doi:10.1155/2011/938240 fatcat:2bgm47lyfva6vi4xhyjtvpmq3a

Design of training populations for selective phenotyping in genomic prediction

Deniz Akdemir, Julio Isidro-Sánchez
2019 Scientific Reports  
When designing the implementation of genomic selection scheme into the breeding cycle, breeders need to select the optimal method for (1) selecting training populations that maximize genomic prediction  ...  Our results show that optimization methods that include information from the test set (targeted) showed the highest accuracies, indicating that apriori information from the TS improves genomic predictions  ...  In Fig. 2 , we also indicated the clustering that best represents the structure in these datasets by plotting genotypes in different clusters and colors.  ... 
doi:10.1038/s41598-018-38081-6 pmid:30723226 pmcid:PMC6363789 fatcat:tcudc5lnqvb57o5zkfqakyb24e

2021 Index IEEE/ACM Transactions on Computational Biology and Bioinformatics Vol. 18

2022 IEEE/ACM Transactions on Computational Biology & Bioinformatics  
-Dec. 2021 2431-2444 Evolving Transcriptomic Profiles From Single-Cell RNA-Seq Data Using Nature-Inspired Multiobjective Optimization. Li, X., +, TCBB Nov.  ...  -Dec. 2021 2431-2444 Evolving Transcriptomic Profiles From Single-Cell RNA-Seq Data Using Nature-Inspired Multiobjective Optimization. Li, X., +, TCBB Nov.  ... 
doi:10.1109/tcbb.2021.3136340 fatcat:bjvb334webfovh4nsc7oeds3di

On the Role of Clustering and Visualization Techniques in Gene Microarray Data

Angelo Ciaramella, Antonino Staiano
2019 Algorithms  
terms of structure, function and evolution.  ...  In particular, we focus on the wide-ranging list of data clustering and visualization techniques able to find homogeneous data groupings, and also provide the possibility to discover its connections in  ...  indices as multiobjective functions simultaneously optimized, in order to properly seize multiple characteristics of the evolving agglomerations.  ... 
doi:10.3390/a12060123 fatcat:bod6vdo4m5dfnhtzsjavqt4viu

2020 Index IEEE Transactions on Neural Networks and Learning Systems Vol. 31

2020 IEEE Transactions on Neural Networks and Learning Systems  
., +, TNNLS Nov. 2020 4968-4979 Deep Subspace Clustering.  ...  Sattler, F., +, TNNLS Sept. 2020 3400-3413 Robust and Communication-Efficient Federated Learning From Non-i.i.d. 3258 Structured Optimal Graph-Based Clustering With Flexible Embedding.  ...  ., +, TNNLS Oct. 2020 3777-3787 On the Working Principle of the Hopfield Neural Networks and its Equivalence to the GADIA in Optimization. Uykan, Z.,  ... 
doi:10.1109/tnnls.2020.3045307 fatcat:34qoykdtarewhdscxqj5jvovqy

Comparing two genetic overproduce-and-choose strategies for fuzzy rule-based multiclassification systems generated by bagging and mutual information-based feature selection

Oscar Cordón, Arnaud Quirin, José M. Benítez, Salvador García, Santi Caballé, Ángel Alejandro Juan
2010 International Journal of Hybrid Intelligent Systems  
In [14] we proposed a scheme to generate fuzzy rule-based multiclassification systems by means of bagging, mutual information-based feature selection, and a multicriteria genetic algorithm for static component  ...  In the current contribution we extend the latter component by making use of the bagging approach's capability to evaluate the accuracy of the classifier ensemble using the out-of-bag estimates.  ...  In [22] a GA is used to select from seven diversity heuristics for k-means cluster-based ensembles and the ensemble size is also encoded in the genome.  ... 
doi:10.3233/his-2010-0104 fatcat:s2rfoiipyrgjnhrws5xw53nsbq

2021 Index IEEE Transactions on Neural Networks and Learning Systems Vol. 32

2021 IEEE Transactions on Neural Networks and Learning Systems  
., +, TNNLS Dec. 2021 5698-5707 Smoothness Regularized Multiview Subspace Clustering With Kernel Learning.  ...  Wang, D., +, TNNLS Feb. 2021 748-762 Robust Kernelized Multiview Self-Representation for Subspace Clustering.  ... 
doi:10.1109/tnnls.2021.3134132 fatcat:2e7comcq2fhrziselptjubwjme
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