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Titlebook: Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics; 9th European Confere Clara Pizzuti,Marylyn D. Ritchie,Mario G

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发表于 2025-3-21 18:17:06 | 显示全部楼层 |阅读模式
书目名称Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
副标题9th European Confere
编辑Clara Pizzuti,Marylyn D. Ritchie,Mario Giacobini
视频video
概述Fast track conference proceedings Unique visibility State of the art research
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics; 9th European Confere Clara Pizzuti,Marylyn D. Ritchie,Mario G
描述This book constitutes the refereed proceedings of the 9th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, EvoBIO 2011, held in Torino, Italy, in April 2011 co-located with the Evo* 2011 events. The 12 revised full papers presented together with 7 poster papers were carefully reviewed and selected from numerous submissions. All papers included topics of interest such as biomarker discovery, cell simulation and modeling, ecological modeling, fluxomics, gene networks, biotechnology, metabolomics, microarray analysis, phylogenetics, protein interactions, proteomics, sequence analysis and alignment, and systems biology.
出版日期Conference proceedings 2011
关键词biological networks analysis; biological organisms; multiobjective optimization; protein analysis; swarm
版次1
doihttps://doi.org/10.1007/978-3-642-20389-3
isbn_softcover978-3-642-20388-6
isbn_ebook978-3-642-20389-3Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Berlin Heidelberg 2011
The information of publication is updating

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发表于 2025-3-21 22:39:50 | 显示全部楼层
A New Evolutionary Gene Regulatory Network Reverse Engineering Tooling genetic programming, it extracts the activation functions of the different genes from those data. Successively, the gene regulatory network is reconstructed exploiting the automatic feature selection performed by genetic programming and its dynamics can be simulated using the previously extracte
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ML-Consensus: A General Consensus Model for Variable-Length Transcription Factor Binding Sitesranscription factor (TF) binding sites (TFBS). Examples include all binding sites being of equal length, or having exactly one core region with fixed format, etc. In this paper, we have constructed a generalized consensus model (called Mixed-Length-Consensus, or ML-Consensus) without such constraint
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ATHENA Optimization: The Effect of Initial Parameter Settings across Different Genetic Modelsor this type of data is the genome-wide association study (GWAS) where each variation is assessed individually for association to disease. While these studies have elucidated novel etiology, much of the variation due to genetics remains unexplained. One hypothesis is that some of the variation lies
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Validating a Threshold-Based Boolean Model of Regulatory Networks on a Biological Organismetwork of a plant, along with the Boolean update functions attached to each element, to validate a previously proposed threshold-based additive update function. To do that, we determine the dynamical regime of the original system, then setup the parameters of the Boolean function to match this regim
发表于 2025-3-22 19:42:31 | 显示全部楼层
A Nearest Neighbour-Based Approach for Viral Protein Structure Prediction This study proposes a method in which protein fragments are assembled according to their physicochemical similarities, using information extracted from known protein structures. Several existing protein tertiary structure prediction methods produce contact maps as their output. Our proposed method
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Finding Motifs in DNA Sequences Applying a Multiobjective Artificial Bee Colony (MOABC) Algorithmk of discovering novel Transcription Factor Binding Sites (TFBS) in DNA sequences. In the last years there have appeared many new evolutionary algorithms based on the collective intelligence. Finding TFBS is crucial for understanding the gene regulatory relationship but, motifs are weakly conserved,
发表于 2025-3-23 09:18:00 | 显示全部楼层
An Evolutionary Approach for Protein Contact Map Predictionording to their importance in the folding process: hydrophobicity, polarity, charge and residue size. Our evolutionary algorithm provides a set of rules which determine different cases where two amino acids are in contact. A rule represents two windows of three amino acids. Each amino acid is charac
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