We apply parameter advising to boost the true accuracy of the Opal aligner 4,5], where the advisor is using parameter sets found by the <\/span>-approximation algorithm. Figure 1 shows the accuracy of the advisor for a parameter set of size k = <\/em>10, where the benchmarks are assigned to bins based on their accuracy using a default Figure 1.<\/strong> Advising accuracy of Facet within benchmark bins<\/strong>.\n <\/p>\n Figure 2 shows the average advising accuracy for parameter sets of various cardinalities using Figure 2.<\/strong> Average advising accuracy of estimators on sets of varying cardinality<\/strong>.\n <\/p>\n Our tool Facet (F<\/strong>eature-based Ac<\/strong>curacy E<\/strong>st<\/strong>imator) 9] is an easy-to-use, open-source utility for estimating the accuracy of a protein multiple Facet is open-source software that allows users to estimate accuracy as either (1) Figure 3.<\/strong> Example of invoking Facet in Java<\/strong>.\n <\/p>\n The Facet website provides parameter sets that can be used with the Opal aligner (namely Parameter advising We apply parameter advising to boost the true accuracy of the Opal aligner 4,5], where the advisor is using parameter sets found by the -approximation algorithm. Figure 1 shows the accuracy of the advisor for a parameter set of size k = 10, where the benchmarks are assigned to bins based on their […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[],"tags":[],"class_list":["post-1925","post","type-post","status-publish","format-standard","hentry"],"_links":{"self":[{"href":"http:\/\/healthmedicinet.com\/news\/wp-json\/wp\/v2\/posts\/1925","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/healthmedicinet.com\/news\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/healthmedicinet.com\/news\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/healthmedicinet.com\/news\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/healthmedicinet.com\/news\/wp-json\/wp\/v2\/comments?post=1925"}],"version-history":[{"count":0,"href":"http:\/\/healthmedicinet.com\/news\/wp-json\/wp\/v2\/posts\/1925\/revisions"}],"wp:attachment":[{"href":"http:\/\/healthmedicinet.com\/news\/wp-json\/wp\/v2\/media?parent=1925"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/healthmedicinet.com\/news\/wp-json\/wp\/v2\/categories?post=1925"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/healthmedicinet.com\/news\/wp-json\/wp\/v2\/tags?post=1925"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
\n parameter choice; the figure also shows the accuracies when using a single default
\n parameter choice, and an oracle. The number of benchmarks per bin is indicated above
\n the columns. An oracle <\/em>is an advisor that knows the true accuracy of an alignment; its accuracy is shown
\n by the dotted line, which gives the performance of a perfect advisor. Notice that
\n in many cases the performance of the estimator is close to the oracle. This is most
\n clear on the bin which has lowest average accuracy, where advising increases the average
\n accuracy by almost 20% compared to using a single default parameter.\n <\/p>\n
\n as the estimator Facet 3], TCS 6], MOS 7], and PredSP 8], where in the average, benchmark bins contribute equally. The vertical axis is advising
\n accuracy on the testing data, averaged over all benchmarks and all folds using 12-fold
\n cross-validation. The horizontal axis is the cardinality k <\/em>of the greedy advisor set. Greedy advisor set found by the approximation algorithm
\n are augmented from the exact set of cardinality ? = 1 (namely, the best single parameter
\n choice). Notice that Facet (the topmost curve in the plot) continues to increase in
\n advising accuracy up to cardinality k <\/em>= 6. Notice also that while all of the advisors reach a plateau, for Facet this occurs
\n at a greater cardinality and accuracy than for other estimators.\n <\/p>\nAccuracy estimation<\/h4>\n
\n sequence alignment. Facet evaluates the estimated accuracy of a computed alignment
\n as a linear combination of real-valued feature functions. We considered 12 features
\n of which we found an optimal subset of 5 that provide the best performance for alignment
\n advising. Many of the most useful features utilize information about protein secondary
\n structure. We find coefficients by fitting the difference in estimator values to the
\n difference in true accuracy for pairs of examples where the correct alignment is known.
\n This \u00e2\u20ac\u0153difference fitting\u00e2\u20ac\u009d approach is computationally efficient and yields an estimator
\n that works well for advising.\n <\/p>\n
\n a stand alone tool, or (2) a software library that can be integrated into a pre-existing
\n Java application. The implementation provides optimized default coefficients and features.
\n These coefficients may also be specified manually and new features can also be added.
\n Figure 3 shows a simple example of using Facet within a Java application to choose between
\n two alignments of the same set of sequences. The secondary structure predictions are
\n computed on the unaligned sequences and can be reused between the two alignments.\n <\/p>\n
\n substitution matrices and affine gap penalties), as well as scripts for structure
\n prediction.\n <\/p>\n","protected":false},"excerpt":{"rendered":"