Parameter advising for multiple sequence alignment /

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Bibliographic Details
Author / Creator:DeBlasio, Dan, author.
Imprint:Cham, Switzerland : Springer, [2017]
©2017
Description:1 online resource
Language:English
Series:Computational biology ; volume 26
Computational biology ; v. 26.
Subject:
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/11451861
Hidden Bibliographic Details
Other authors / contributors:Kececioglu, John, author.
ISBN:9783319649184
3319649183
9783319649177
3319649175
Digital file characteristics:text file PDF
Notes:Includes bibliographical references and indexes.
Online resource; title from PDF title page (EBSCO, viewed January 11, 2018).
Summary: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.
Other form:Print version: DeBlasio, Dan. Parameter advising for multiple sequence alignment. Cham, Switzerland : Springer, [2017] 3319649175 9783319649177
Standard no.:10.1007/978-3-319-64918-4

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505 0 |a 1 Introduction and Background -- 2 Alignment Accuracy Estimation -- 3 The Facet Estimator -- 4 Computational Complexity of Advising -- 5 Constructing Advisors -- 6 Parameter Advising for the Opal Aligner -- 7 Ensemble Mind Alignment -- 8 Adaptive Local Realignment -- 9 Core Column Prediction for Alignments -- 10 Future Directions. 
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