Evolutionary Computation Machine Learning And Data Mining In Bioinformatics
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Evolutionary Computation Machine Learning And Data Mining In Bioinformatics
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Author :
language : en
Publisher: Springer
Release Date : 2013-02-27
Evolutionary Computation Machine Learning And Data Mining In Bioinformatics written by and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-02-27 with categories.
Evolutionary Computation Machine Learning And Data Mining In Bioinformatics
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Author : Elena Marchiori
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-04-02
Evolutionary Computation Machine Learning And Data Mining In Bioinformatics written by Elena Marchiori and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-04-02 with Computers categories.
This book constitutes the refereed proceedings of the 5th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, EvoBIO 2007, held in Valencia, Spain, April 2007. Coverage brings together experts in computer science with experts in bioinformatics and the biological sciences. It presents contributions on fundamental and theoretical issues along with papers dealing with different applications areas.
Evolutionary Computation Machine Learning And Data Mining In Bioinformatics
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Author : Mario Giacobini
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-03-28
Evolutionary Computation Machine Learning And Data Mining In Bioinformatics written by Mario Giacobini and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-03-28 with Computers categories.
This book constitutes the refereed proceedings of the 10th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, EvoBIO 2012, held in Málaga, Spain, in April 2012 co-located with the Evo* 2012 events. The 15 revised full papers presented together with 8 poster papers were carefully reviewed and selected from numerous submissions. Computational Biology is a wide and varied discipline, incorporating aspects of statistical analysis, data structure and algorithm design, machine learning, and mathematical modeling toward the processing and improved understanding of biological data. Experimentalists now routinely generate new information on such a massive scale that the techniques of computer science are needed to establish any meaningful result. As a consequence, biologists now face the challenges of algorithmic complexity and tractability, and combinatorial explosion when conducting even basic analyses.
Evolutionary Computation Machine Learning And Data Mining In Bioinformatics
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Author : Clara Pizzuti
language : en
Publisher: Springer Science & Business Media
Release Date : 2009-04-02
Evolutionary Computation Machine Learning And Data Mining In Bioinformatics written by Clara Pizzuti and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-04-02 with Computers categories.
This book constitutes the refereed proceedings of the 7th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, EvoBIO 2009, held in Tübingen, Germany, in April 2009 colocated with the Evo* 2009 events. The 17 revised full papers were carefully reviewed and selected from 44 submissions. EvoBio is the premiere European event for experts in computer science meeting with experts in bioinformatics and the biological sciences, all interested in the interface between evolutionary computation, machine learning, data mining, bioinformatics, and computational biology. Topics addressed by the papers include biomarker discovery, cell simulation and modeling, ecological modeling, uxomics, gene networks, biotechnology, metabolomics, microarray analysis, phylogenetics, protein interactions, proteomics, sequence analysis and alignment, as well as systems biology.
Evolutionary Computation Machine Learning And Data Mining In Bioinformatics
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Author : Marylyn D. Ritchie
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-03-25
Evolutionary Computation Machine Learning And Data Mining In Bioinformatics written by Marylyn D. Ritchie and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-03-25 with Computers categories.
The ?eld of bioinformatics has two main objectives: the creation and main- nance of biological databases, and the discovery of knowledge from life sciences datainordertounravelthemysteriesofbiologicalfunction,leadingtonewdrugs andtherapiesforhumandisease. Life sciencesdatacomeinthe formofbiological sequences, structures, pathways, or literature. One major aspect of discovering biological knowledge is to search, predict, or model speci'c information in a given dataset in order to generate new interesting knowledge. Computer science methods such as evolutionary computation, machine learning, and data mining all have a great deal to o'er the ?eld of bioinformatics. The goal of the 8th - ropean Conference on Evolutionary Computation, Machine Learning, and Data Mining in Bioinformatics (EvoBIO 2010) was to bring together experts in these ?elds in order to discuss new and novel methods for tackling complex biological problems. The 8th EvoBIO conference was held in Istanbul, Turkey during April 7-9, 2010attheIstanbulTechnicalUniversity. EvoBIO2010washeldjointlywiththe 13th European Conference on Genetic Programming (EuroGP 2010), the 10th European Conference on Evolutionary Computation in Combinatorial Opti- sation (EvoCOP 2010), and the conference on the applications of evolutionary computation,EvoApplications. Collectively,the conferences areorganizedunder the name Evo* (www. evostar. org). EvoBIO, held annually as a workshop since 2003, became a conference in 2007 and it is now the premiere European event for those interested in the interface between evolutionary computation, machine learning, data mining, bioinformatics, and computational biology.
Evolutionary Computation Machine Learning And Data Mining In Bioinformatics
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Author : Clara Pizzuti
language : en
Publisher: Springer
Release Date : 2011-04-27
Evolutionary Computation Machine Learning And Data Mining In Bioinformatics written by Clara Pizzuti and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-04-27 with Computers categories.
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.
Evolutionary Computation Machine Learning And Data Mining In Bioinformatics
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Author : Leonardo Vanneschi
language : en
Publisher: Springer
Release Date : 2013-02-26
Evolutionary Computation Machine Learning And Data Mining In Bioinformatics written by Leonardo Vanneschi and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-02-26 with Computers categories.
This book constitutes the refereed proceedings of the 11th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, EvoBIO 2013, held in Vienna, Austria, in April 2013, colocated with the Evo* 2013 events EuroGP, EvoCOP, EvoMUSART and EvoApplications. The 10 revised full papers presented together with 9 poster papers were carefully reviewed and selected from numerous submissions. The papers cover a wide range of topics in the field of biological data analysis and computational biology. They address important problems in biology, from the molecular and genomic dimension to the individual and population level, often drawing inspiration from biological systems in oder to produce solutions to biological problems.
Evolutionary Computation Machine Learning And Data Mining In Bioinformatics
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Author : Elena Marchiori
language : en
Publisher: Springer
Release Date : 2008-04-03
Evolutionary Computation Machine Learning And Data Mining In Bioinformatics written by Elena Marchiori and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-04-03 with Computers categories.
Coverage in this proceedings volume includes biomarker discovery, cell simulation and modeling, ecological modeling, gene networks, biotechnology, microarray analysis, protein interactions, proteomics, sequence analysis and alignment, and systems biology
Evolutionary Computation In Data Mining
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Author : Ashish Ghosh
language : en
Publisher: Springer
Release Date : 2006-06-22
Evolutionary Computation In Data Mining written by Ashish Ghosh and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-06-22 with Computers categories.
Data mining (DM) consists of extracting interesting knowledge from re- world, large & complex data sets; and is the core step of a broader process, called the knowledge discovery from databases (KDD) process. In addition to the DM step, which actually extracts knowledge from data, the KDD process includes several preprocessing (or data preparation) and post-processing (or knowledge refinement) steps. The goal of data preprocessing methods is to transform the data to facilitate the application of a (or several) given DM algorithm(s), whereas the goal of knowledge refinement methods is to validate and refine discovered knowledge. Ideally, discovered knowledge should be not only accurate, but also comprehensible and interesting to the user. The total process is highly computation intensive. The idea of automatically discovering knowledge from databases is a very attractive and challenging task, both for academia and for industry. Hence, there has been a growing interest in data mining in several AI-related areas, including evolutionary algorithms (EAs). The main motivation for applying EAs to KDD tasks is that they are robust and adaptive search methods, which perform a global search in the space of candidate solutions (for instance, rules or another form of knowledge representation).
Automating The Design Of Data Mining Algorithms
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Author : Gisele L. Pappa
language : en
Publisher: Springer Science & Business Media
Release Date : 2009-10-27
Automating The Design Of Data Mining Algorithms written by Gisele L. Pappa and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-10-27 with Computers categories.
Data mining is a very active research area with many successful real-world app- cations. It consists of a set of concepts and methods used to extract interesting or useful knowledge (or patterns) from real-world datasets, providing valuable support for decision making in industry, business, government, and science. Although there are already many types of data mining algorithms available in the literature, it is still dif cult for users to choose the best possible data mining algorithm for their particular data mining problem. In addition, data mining al- rithms have been manually designed; therefore they incorporate human biases and preferences. This book proposes a new approach to the design of data mining algorithms. - stead of relying on the slow and ad hoc process of manual algorithm design, this book proposes systematically automating the design of data mining algorithms with an evolutionary computation approach. More precisely, we propose a genetic p- gramming system (a type of evolutionary computation method that evolves c- puter programs) to automate the design of rule induction algorithms, a type of cl- si cation method that discovers a set of classi cation rules from data. We focus on genetic programming in this book because it is the paradigmatic type of machine learning method for automating the generation of programs and because it has the advantage of performing a global search in the space of candidate solutions (data mining algorithms in our case), but in principle other types of search methods for this task could be investigated in the future.