Kececioglu john (6 Ergebnisse)
Sprache: Englisch
Verlag: Springer 2019
Serie: Computational Biology, Buch 13 von 21. Buch 13 von 21 - Computational Biology
- Softcover
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Zustand: New. In.
Sprache: Englisch
Verlag: Springer 2018
Serie: Computational Biology, Buch 13 von 21. Buch 13 von 21 - Computational Biology
- Hardcover
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes KönigreichRia Christie Collections
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EUR 13,88 VersandVersand von Vereinigtes Königreich nach USAAnzahl: Mehr als 20 verfügbar
Zustand: New. In.
Sprache: Englisch
Verlag: Springer-Verlag New York Inc 2019
Serie: Computational Biology, Buch 13 von 21. Buch 13 von 21 - Computational Biology
- Softcover
Anbieter: Revaluation Books, Exeter, Vereinigtes KönigreichRevaluation Books
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Paperback. Zustand: Brand New. reprint edition. 168 pages. 9.25x6.10x0.40 inches. In Stock.
Sprache: Englisch
Verlag: Springer-Verlag New York Inc 2018
Serie: Computational Biology, Buch 13 von 21. Buch 13 von 21 - Computational Biology
- Hardcover
Anbieter: Revaluation Books, Exeter, Vereinigtes KönigreichRevaluation Books
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Hardcover. Zustand: Brand New. 166 pages. 9.25x6.10x0.63 inches. In Stock.
Sprache: Englisch
Verlag: Springer International Publishing, Springer International Publishing 2019
Serie: Computational Biology, Buch 13 von 21. Buch 13 von 21 - Computational Biology
- Softcover
Anbieter: AHA-BUCH GmbH, Einbeck, DeutschlandAHA-BUCH GmbH
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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 automatic…ally 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.
Sprache: Englisch
Verlag: Springer International Publishing 2018
Serie: Computational Biology, Buch 13 von 21. Buch 13 von 21 - Computational Biology
- Hardcover
Anbieter: AHA-BUCH GmbH, Einbeck, DeutschlandAHA-BUCH GmbH
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Neu
EUR 53,49
EUR 62,12 VersandVersand von Deutschland nach USAAnzahl: 1 verfügbar
Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 ch…ooses 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.
