{"product_id":"parameter-advising-for-multiple-sequence-alignment-von-dan-deblasio-john-d-kececioglu","title":"Parameter Advising for Multiple Sequence Alignment","description":"\n                                \n                \u003cp\u003e\n                                        This book develops a new approach called \n                    \n                    \u003ci\u003eparameter advising\u003c\/i\u003e\n                                         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\n                    \n                    \u003ci\u003e advisor\u003c\/i\u003e\n                                         is a procedure that automatically chooses a parameter setting for the input, and has two main ingredients:\n                \n                \u003c\/p\u003e\n                                    \n                \n                \u003cp\u003e\n                                        (a)         the \n                    \n                    \u003ci\u003eset \u003c\/i\u003e\n                                        of parameter choices considered by the advisor, and\n                \n                \u003c\/p\u003e\n                                  \n                \n                \u003cp\u003e\n                                        (b)         an \n                    \n                    \u003ci\u003eestimator\u003c\/i\u003e\n                                         of alignment accuracy used to rank alignments produced by the aligner.\n                \n                \u003c\/p\u003e\n                                    \n                \n                \u003cp\u003eOn 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.\u003c\/p\u003e\n                                    \n                \n                \u003cp\u003eThe chapters first lay out the foundations of parameter advising, and then cover applications and extensions of advising. The content\u003c\/p\u003e\n                                    \n                \n                \u003cp\u003e\n                                        •   examines formulations of parameter advising and their \n                    \n                    \u003ci\u003ecomputational complexity\u003c\/i\u003e\n                                        ,\n                \n                \u003c\/p\u003e\n                                  \n                \n                \u003cp\u003e\n                                        •   develops methods for learning good \n                    \n                    \u003ci\u003eaccuracy estimators\u003c\/i\u003e\n                                        ,\n                \n                \u003c\/p\u003e\n                                  \n                \n                \u003cp\u003e\n                                        •   presents approximation algorithms for finding good sets of \n                    \n                    \u003ci\u003eparameter choices\u003c\/i\u003e\n                                        , and \n                \n                \u003c\/p\u003e\n                                  \n                \n                \u003cp\u003e\n                                        •   assesses \n                    \n                    \u003ci\u003esoftware implementations\u003c\/i\u003e\n                                         of advising that perform well on real biological data.\n                \n                \u003c\/p\u003e\n                                    \n                \n                \u003cp\u003eAlso explored are applications of parameter advising to\u003c\/p\u003e\n                                    \n                \n                \u003cp\u003e\n                                        •   \n                    \n                    \u003ci\u003eadaptive local realignment\u003c\/i\u003e\n                                        , where advising is performed on local regions of the sequences to automatically adapt to varying mutation rates, and\n                \n                \u003c\/p\u003e\n                                  \n                \n                \u003cp\u003e\n                                        •   \n                    \n                    \u003ci\u003eensemble alignment\u003c\/i\u003e\n                                        , 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.\n                \n                \u003c\/p\u003e\n                                    \n                \n                \u003cp\u003eThe book concludes by offering future directions in advising research.\u003c\/p\u003e\n                            \n            \u003cdiv class=\"aw-variant-hidden-subtitle-div\" id=\"aw-variant-subtitle-9783319879024\"\u003e\u003ch3\u003e\u003c\/h3\u003e\u003c\/div\u003e\u003cdiv class=\"aw-variant-hidden-subtitle-div\" id=\"aw-variant-subtitle-9783319649177\"\u003e\u003ch3\u003e\u003c\/h3\u003e\u003c\/div\u003e","brand":"Libri","offers":[{"title":"Softcover - 9783319879024","offer_id":39422365302877,"sku":"9783319879024","price":53.49,"currency_code":"EUR","in_stock":true},{"title":"Hardcover - 9783319649177","offer_id":51489654022,"sku":"9783319649177","price":53.49,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0940\/0622\/files\/378684eb-58b5-406e-8aa7-7309998cc0f1.jpg?v=1775708494","url":"https:\/\/shop.autorenwelt.de\/products\/parameter-advising-for-multiple-sequence-alignment-von-dan-deblasio-john-d-kececioglu","provider":"Autorenwelt Shop","version":"1.0","type":"link"}