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Efficient planning and superior execution against clear objectives is the way companies operate best. For many companies, the planning process is carefully orchestrated, objectives are set, budgets are negotiated, resources are allocated, and then it's up to the organization to execute. But what happens when the unexpected occurs? Say, an event occurs that throws the status quo into turmoil. What if your business declines by 40% in 1 month, what do you do? Conversely, what if your demand spikes by 50% in just a few months, what then? Suppose a natural disaster happens, or a new technology creates a significant, but unplanned opportunity? We call these events 'stretch opportunities'. This boo...
Here are the refereed proceedings of the 6th International Workshop on Algorithms in Bioinformatics, WABI 2006, held in the course of the ALGO 2006 conference meetings. The book presents 36 revised full papers addressing all current issues of algorithms in bioinformatics, from mathematical tools to experimental studies of approximation algorithms and reports on significant computational analyses. For the first time, coverage extends to machine-learning approaches along with combinatorial optimization.
This volume contains the proceedings of the 15th Annual International Sym- sium on Algorithms and Computation (ISAAC 2004), held in Hong Kong, 20–22 December, 2004. In the past, it has been held in Tokyo (1990), Taipei (1991), Nagoya (1992), Hong Kong (1993), Beijing (1994), Cairns (1995), Osaka (1996), Singapore (1997), Taejon (1998), Chennai (1999), Taipei (2000), Christchurch (2001), Vancouver (2002), and Kyoto (2003). ISAAC is an annual international symposium that covers a wide range of topics,namelyalgorithmsandcomputation.Themainpurposeofthesymposium is to provide a forum for researchers working in the active research community of algorithms and the theory of computation to present ...
Learn how to apply rough-fuzzy computing techniques to solve problems in bioinformatics and medical image processing Emphasizing applications in bioinformatics and medical image processing, this text offers a clear framework that enables readers to take advantage of the latest rough-fuzzy computing techniques to build working pattern recognition models. The authors explain step by step how to integrate rough sets with fuzzy sets in order to best manage the uncertainties in mining large data sets. Chapters are logically organized according to the major phases of pattern recognition systems development, making it easier to master such tasks as classification, clustering, and feature selection....
Annotation This book constitutes the refereed proceedings of the 21st International Symposium on Algorithms and Computation, ISAAC 2010, held in Jeju, South Korea in December 2010.The 77 revised full papers presented were carefully reviewed and selected from 182 submissions for inclusion in the book. This volume contains topics such as approximation algorithm; complexity; data structure and algorithm; combinatorial optimization; graph algorithm; computational geometry; graph coloring; fixed parameter tractability; optimization; online algorithm; and scheduling.
An invaluable tool in Bioinformatics, this unique volume provides both theoretical and experimental results, and describes basic principles of computational intelligence and pattern analysis while deepening the reader's understanding of the ways in which these principles can be used for analyzing biological data in an efficient manner. This book synthesizes current research in the integration of computational intelligence and pattern analysis techniques, either individually or in a hybridized manner. The purpose is to analyze biological data and enable extraction of more meaningful information and insight from it. Biological data for analysis include sequence data, secondary and tertiary str...
This book constitutes the refereed proceedings of the 9th International Symposium on Bioinformatics Research and Applications, ISBRA 2013, held in Charlotte, NC, USA, in May 2013. The 25 revised full papers presented together with 4 invited talks were carefully reviewed and selected from 46 submissions. The papers cover a wide range of biomedical databases and data integration, high-performance bio-computing, biomolecular imaging, high-throughput sequencing data analysis, bio-ontologies, molecular evolution, comparative genomics and phylogenomics, molecular modeling and simulation, pattern discovery and classification, computational proteomics, population genetics, data mining and visualization, software tools and applications.
An introduction to machine learning methods and their applications to problems in bioinformatics Machine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel computational techniques to analyze high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. Machine learning techniques such as Markov models, support vector machines, neural networks, and graphical models have been successful in analyzing life science data because of their capabilities in handling randomness and uncertainty of data noise and in generalizatio...