{"title":"Dan Deblasio","description":null,"products":[{"product_id":"parameter-advising-for-multiple-sequence-alignment-book-dan-deblasio-9783319649177","title":"Parameter Advising for Multiple Sequence Alignment","description":"\u003cp\u003eThis book develops a new approach called \u003ci\u003eparameter advising\u003c\/i\u003e 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\u003ci\u003e advisor\u003c\/i\u003e is a procedure that automatically chooses a parameter setting for the input, and has two main ingredients:\u003c\/p\u003e    \u003cp\u003e(a)         the \u003ci\u003eset \u003c\/i\u003eof parameter choices considered by the advisor, and\u003c\/p\u003e  \u003cp\u003e(b)         an \u003ci\u003eestimator\u003c\/i\u003e of alignment accuracy used to rank alignments produced by the aligner.\u003c\/p\u003e    \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    \u003cp\u003eThe chapters first lay out the foundations of parameter advising, and then cover applications and extensions of advising. The content\u003c\/p\u003e    \u003cp\u003e•   examines formulations of parameter advising and their \u003ci\u003ecomputational complexity\u003c\/i\u003e,\u003c\/p\u003e  \u003cp\u003e•   develops methods for learning good \u003ci\u003eaccuracy estimators\u003c\/i\u003e,\u003c\/p\u003e  \u003cp\u003e•   presents approximation algorithms for finding good sets of \u003ci\u003eparameter choices\u003c\/i\u003e, and \u003c\/p\u003e  \u003cp\u003e•   assesses \u003ci\u003esoftware implementations\u003c\/i\u003e of advising that perform well on real biological data.\u003c\/p\u003e    \u003cp\u003eAlso explored are applications of parameter advising to\u003c\/p\u003e    \u003cp\u003e•   \u003ci\u003eadaptive local realignment\u003c\/i\u003e, where advising is performed on local regions of the sequences to automatically adapt to varying mutation rates, and\u003c\/p\u003e  \u003cp\u003e•   \u003ci\u003eensemble alignment\u003c\/i\u003e, 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.\u003c\/p\u003e    \u003cp\u003eThe book concludes by offering future directions in advising research.\u003c\/p\u003e","brand":"WoB","offers":[{"title":"GB \/ NEW \/ INGRAM","offer_id":52137876717841,"sku":"NLS9783319649177","price":0.0,"currency_code":"GBP","in_stock":true},{"title":"US \/ NEW \/ INGRAM","offer_id":52760983830801,"sku":"NIN9783319649177","price":0.0,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/9783319649177.jpg?v=1785872594"},{"product_id":"parameter-advising-for-multiple-sequence-alignment-book-dan-deblasio-9783319879024","title":"Parameter Advising for Multiple Sequence Alignment","description":"\u003cp\u003eThis book develops a new approach called \u003ci\u003eparameter advising\u003c\/i\u003e 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\u003ci\u003e advisor\u003c\/i\u003e is a procedure that automatically chooses a parameter setting for the input, and has two main ingredients:\u003c\/p\u003e    \u003cp\u003e(a)         the \u003ci\u003eset \u003c\/i\u003eof parameter choices considered by the advisor, and\u003c\/p\u003e  \u003cp\u003e(b)         an \u003ci\u003eestimator\u003c\/i\u003e of alignment accuracy used to rank alignments produced by the aligner.\u003c\/p\u003e    \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    \u003cp\u003eThe chapters first lay out the foundations of parameter advising, and then cover applications and extensions of advising. The content\u003c\/p\u003e    \u003cp\u003e•   examines formulations of parameter advising and their \u003ci\u003ecomputational complexity\u003c\/i\u003e,\u003c\/p\u003e  \u003cp\u003e•   develops methods for learning good \u003ci\u003eaccuracy estimators\u003c\/i\u003e,\u003c\/p\u003e  \u003cp\u003e•   presents approximation algorithms for finding good sets of \u003ci\u003eparameter choices\u003c\/i\u003e, and \u003c\/p\u003e  \u003cp\u003e•   assesses \u003ci\u003esoftware implementations\u003c\/i\u003e of advising that perform well on real biological data.\u003c\/p\u003e    \u003cp\u003eAlso explored are applications of parameter advising to\u003c\/p\u003e    \u003cp\u003e•   \u003ci\u003eadaptive local realignment\u003c\/i\u003e, where advising is performed on local regions of the sequences to automatically adapt to varying mutation rates, and\u003c\/p\u003e  \u003cp\u003e•   \u003ci\u003eensemble alignment\u003c\/i\u003e, 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.\u003c\/p\u003e    \u003cp\u003eThe book concludes by offering future directions in advising research.\u003c\/p\u003e","brand":"WoB","offers":[{"title":"GB \/ NEW \/ INGRAM","offer_id":52600102519057,"sku":"NLS9783319879024","price":0.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/9783319879024.jpg?v=1787053240"}],"url":"https:\/\/www.worldofbooks.com\/en-au\/collections\/author-books-by-dan-deblasio.oembed","provider":"World of Books ","version":"1.0","type":"link"}