Parameter Advising for Multiple Sequence Alignment, Deblasio Dan, Kececioglu John
Автор: Hans Georg Bock; Thomas Carraro; Willi J?ger; Stef Название: Model Based Parameter Estimation ISBN: 3642440762 ISBN-13(EAN): 9783642440762 Издательство: Springer Рейтинг: Цена: 18167.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book features papers from a workshop on parameter estimation held in 2009 in Heidelberg. It combines mathematical and numerical methods to apply parameter estimation and optimum experimental design in a range of contexts.
Автор: M.C.K. Khoo Название: Modeling and Parameter Estimation in Respiratory Control ISBN: 1461278961 ISBN-13(EAN): 9781461278962 Издательство: Springer Рейтинг: Цена: 15672.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Bernd Hofmann; Antonio Leit?o; Jorge P. Zubelli Название: New Trends in Parameter Identification for Mathematical Models ISBN: 3319889982 ISBN-13(EAN): 9783319889986 Издательство: Springer Рейтинг: Цена: 16769.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The Proceedings volume contains 16 contributions to the IMPA conference “New Trends in Parameter Identification for Mathematical Models”, Rio de Janeiro, Oct 30 – Nov 3, 2017, integrating the “Chemnitz Symposium on Inverse Problems on Tour”. This conference is part of the “Thematic Program on Parameter Identification in Mathematical Models” organized at IMPA in October and November 2017. One goal is to foster the scientific collaboration between mathematicians and engineers from the Brazialian, European and Asian communities. Main topics are iterative and variational regularization methods in Hilbert and Banach spaces for the stable approximate solution of ill-posed inverse problems, novel methods for parameter identification in partial differential equations, problems of tomography , solution of coupled conduction-radiation problems at high temperatures, and the statistical solution of inverse problems with applications in physics.
Описание: This comprehensive book, rich with applications, offers a quantitative framework for the analysis of the various capture-recapture models for open animal populations, while also addressing associated computational methods. The state of our wildlife populations provides a litmus test for the state of our environment, especially in light of global warming and the increasing pollution of our land, seas, and air. In addition to monitoring our food resources such as fisheries, we need to protect endangered species from the effects of human activities (e.g. rhinos, whales, or encroachments on the habitat of orangutans). Pests must be be controlled, whether insects or viruses, and we need to cope with growing feral populations such as opossums, rabbits, and pigs.Accordingly, we need to obtain information about a given population’s dynamics, concerning e.g. mortality, birth, growth, breeding, sex, and migration, and determine whether the respective population is increasing , static, or declining. There are many methods for obtaining population information, but the most useful (and most work-intensive) is generically known as “capture-recapture,” where we mark or tag a representative sample of individuals from the population and follow that sample over time using recaptures, resightings, or dead recoveries. Marks can be natural, such as stripes, fin profiles, and even DNA; or artificial, such as spots on insects. Attached tags can, for example, be simple bands or streamers, or more sophisticated variants such as radio and sonic transmitters. To estimate population parameters, sophisticated and complex mathematical models have been devised on the basis of recapture information and computer packages. This book addresses the analysis of such models. It is primarily intended for ecologists and wildlife managers who wish to apply the methods to the types of problems discussed above, though it will also benefit researchers and graduate students in ecology. Familiarity with basic statistical concepts is essential.
Автор: Kaltenbacher Barbara, Schuster Thomas, Wald Anne Название: Time-Dependent Problems in Imaging and Parameter Identification ISBN: 303057783X ISBN-13(EAN): 9783030577834 Издательство: Springer Цена: 20962.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: 1 Joint phase reconstruction and magnitude segmentation from velocity-encoded MRI data 2 Dynamic Inverse Problems for the Acoustic Wave Equation 3 Motion compensation strategies in tomography 4 Microlocal properties of dynamic Fourier integral operators 5 The tangential cone condition for some coefficient identification model problems in parabolic PDEs 6 Sequential subspace optimization for recovering stored energy functions in hyperelastic materials from time-dependent data 7 Joint Motion Estimation and Source Identification using Convective Regularisation with an Application to the Analysis of Laser Nanoablations 8 Quantitative OCT reconstructions for dispersive media 9 Review of Image Similarity Measures for Joint Image Reconstruction from Multiple Measurements 10 Holmgren-John Unique Continuation Theorem for Viscoelastic Systems 11 Tomographic Reconstruction for Single Conjugate Adaptive Optics 12 Inverse Problems of Single Molecule Localization Microscopy 13 Parameter identification for the Landau-Lifshitz-Gilbert equation in Magnetic Particle Imaging 14 An inverse source problem related to acoustic nonlinearity parameter imaging
Автор: Dan DeBlasio; John Kececioglu Название: Parameter Advising for Multiple Sequence Alignment ISBN: 3319649175 ISBN-13(EAN): 9783319649177 Издательство: Springer Рейтинг: Цена: 12577.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
This book develops a new approach called parameter advising for finding a parameter setting for a sequence aligner that yields a quality alignment of a given set of input sequences. In this framework, a parameter advisor is a procedure that automatically chooses a parameter setting for the input, and has two main ingredients:
(a) the set of parameter choices considered by the advisor, and
(b) an estimator of alignment accuracy used to rank alignments produced by the aligner.
On coupling a parameter advisor with an aligner, once the advisor is trained in a learning phase, the user simply inputs sequences to align, and receives an output alignment from the aligner, where the advisor has automatically selected the parameter setting.
The chapters first lay out the foundations of parameter advising, and then cover applications and extensions of advising. The content
- examines formulations of parameter advising and their computational complexity,
- develops methods for learning good accuracy estimators,
- presents approximation algorithms for finding good sets of parameter choices, and
- assesses software implementations of advising that perform well on real biological data.
Also explored are applications of parameter advising to
- adaptive local realignment, where advising is performed on local regions of the sequences to automatically adapt to varying mutation rates, and
- ensemble alignment, where advising is applied to an ensemble of aligners to effectively yield a new aligner of higher quality than the individual aligners in the ensemble.
The book concludes by offering future directions in advising research.
Описание: This monograph provides a comprehensive overview of methods for searching, evaluating, and optimizing highway location and alignments using genetic algorithms (GAs), a powerful Artificial Intelligence (AI) technique. It presents a two-level programming structure to deal with the effects of varying highway location on traffic level changes in surrounding road networks within the highway location search and alignment optimization process. In addition, the proposed method evaluates environmental impacts as well as all relevant highway costs associated with its construction, operation, and maintenance. The monograph first covers various search methods, relevant cost functions, constraints, computational efficiency, and solution quality issues arising from optimizing the highway alignment optimization (HAO) problem. It then focuses on applications of a special-purpose GA in the HAO problem where numerous highway alignments are generated and evaluated, and finally the best ones are selected based on costs, traffic impacts, safety, energy, and environmental considerations. A review of other promising optimization methods for the HAO problem is also provided in this monograph.
Описание: Covers the fundamentals and techniques of multiple biological sequence alignment and analysis, and shows readers how to choose the appropriate sequence analysis tools for their tasks This book describes the traditional and modern approaches in biological sequence alignment and homology search.
Автор: David J Russell Название: Multiple Sequence Alignment Methods ISBN: 1493960474 ISBN-13(EAN): 9781493960477 Издательство: Springer Рейтинг: Цена: 15372.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Authoritative and practical, Multiple Sequence Alignment Methods provides a readily available resource which will allow practitioners to experiment with different algorithms and find the particular algorithm that is of most use in their application.
Автор: David J Russell Название: Multiple Sequence Alignment Methods ISBN: 1627036458 ISBN-13(EAN): 9781627036450 Издательство: Springer Рейтинг: Цена: 15372.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Authoritative and practical, Multiple Sequence Alignment Methods provides a readily available resource which will allow practitioners to experiment with different algorithms and find the particular algorithm that is of most use in their application.
Автор: Kazutaka Katoh Название: Multiple Sequence Alignment ISBN: 107161035X ISBN-13(EAN): 9781071610350 Издательство: Springer Рейтинг: Цена: 16769.00 р. Наличие на складе: Поставка под заказ.
Описание: Chapter "Alignment of Biological Sequences with Jalview" is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
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