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Copied from Raj's patent collection ' CSIRO Patent'.
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Search applicants and Owners(US) repectively = ' Commonw* Scie* Indu* Res* Org* ', 'Commonw* Scien* Indus* Res* NEAR Aus*', 'Commonw* Scien* Indus* Res* Orga*', \"Com* Sci* Ind* Res* Org*\", 'Com* Sci* AND Ind* Res* Org*'
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(a) parameter model fitting the melt curve data;
(b) performing a principal component analysis of the melt curve data; and
(c) utilising the principal components for clustering the melt curve data into groups."],"number":1,"annotation":false,"title":false,"claim":true},{"lines":["The method according to claim 1 wherein the step (a) comprises fitting the melt curve data to a model of the form: A + B - CT 1 + exp λ T - T 0 where A is an estimate of the minimum observed fluorescence; B and C are estimates of the intercept and slope of the initial part of the melt curve; T0 is estimated as the point of maximum (negative) slope of the melt curve and λ is approximately 2."],"number":2,"annotation":false,"title":false,"claim":true},{"lines":["The method according to claim 1, wherein the step (a) comprises a scaling of the melt curve to a line of best fit."],"number":3,"annotation":false,"title":false,"claim":true},{"lines":["The method according to claim 3, wherein a region of melt curve data prior to the melt curve transition and a region of melt curve data following the melt curve transition are selected and an average fluorescence value is calculated."],"number":4,"annotation":false,"title":false,"claim":true},{"lines":["The method according to any one of the preceding claims, wherein at least the first two principal components are utilised in step (c)."],"number":5,"annotation":false,"title":false,"claim":true},{"lines":["The method according to any one of the preceding claims, wherein said clustering occurs by means of k-means clustering or hierarchical clustering for unsupervised melt curve data."],"number":6,"annotation":false,"title":false,"claim":true},{"lines":["The method according to claim 6, wherein a gap statistic is utilised for determining the number of groups for the melt curve data."],"number":7,"annotation":false,"title":false,"claim":true},{"lines":["The method according to claim 7, wherein determining the number of groups for the melt curve data comprises the comparison of a measure of cluster quality to data known not to have any real clusters."],"number":8,"annotation":false,"title":false,"claim":true},{"lines":["The method according to claim 7 or 8, wherein the measure of cluster quality is determined applying the formula: W k = ∑ j = 1 k ∑ i ∈ c j ‖ x i - x ‾ j ‖ 2 where Cj is the j-th cluster, and x j is its centre."],"number":9,"annotation":false,"title":false,"claim":true},{"lines":["The method according to any one of claims 7 to 9, wherein determining the number of groups for the melt curve data comprises the steps of:
(i) clustering the observed data, varying the number of clusters k = 1, 2, ..., K for a maximum K and computing Wk for each k;
(ii) generating B random reference data sets and clustering each as in step (i), computing Wkb for k = 1, 2, ..., K and b = 1, 2, ..., B;
(iii) computing the Gap statistic and standard error for each k applying the formulae Gap k = 1 / B ∑ log W k b - log W k and s k = 1 + 1 / B { 1 / B ∑ b ( log W k b - 1 / B ∑ b log W k b ) 2 ;
(iv) choosing the number of clusters k̂ to be the smallest k such that Gap(k) ≥ Gap(k+1) - sk+1."],"number":10,"annotation":false,"title":false,"claim":true},{"lines":["The method according to any one of the preceding claims, wherein a Bayes Information Criterion analysis is utilised for determining the number of groups for the melt curve data."],"number":11,"annotation":false,"title":false,"claim":true},{"lines":["The method according to any one of the preceding claims, wherein said melt curve data is for a known substance and said clustering utilises linear discriminant analysis to cluster the melt curve data into groups."],"number":12,"annotation":false,"title":false,"claim":true},{"lines":["The method according to any one of the preceding claims, wherein said clustering utilises a typicality index to assign the melt curve data into a group."],"number":13,"annotation":false,"title":false,"claim":true},{"lines":["The method according to any one of the preceding claims, wherein said melt curve and said clustering utilise posterior class probabilities to cluster the melt curve data into groups."],"number":14,"annotation":false,"title":false,"claim":true},{"lines":["A computer program comprising means for performing the method according to any one of the preceding claims."],"number":15,"annotation":false,"title":false,"claim":true},{"lines":["The computer program according to claim 15, comprising import means arranged to import raw high resolution melting data and parsing means arranged to syntactically analysing the imported data."],"number":16,"annotation":false,"title":false,"claim":true},{"lines":["The computer program according to claim 15 or 16, comprising cropping means arranged to allow a user to avoid regions of non-specific amplification products."],"number":17,"annotation":false,"title":false,"claim":true},{"lines":["The computer program according to any one of the claims 15 to 17, comprising sample selection means and mode selection means arranged to allow a user to select between analysing a supervised data set or an unsupervised data set following the sample selection."],"number":18,"annotation":false,"title":false,"claim":true},{"lines":["The computer program according to claim 18, comprising parameter selection means arranged to select a most appropriate cluster number and a most appropriate principal component number via k-means clustering and Gap statistics when the user selects analysing an unsupervised data set."],"number":19,"annotation":false,"title":false,"claim":true},{"lines":["The computer program according to claim 18 or 19, comprising control selection means arranged to allow the user to select the appropriate controls for each genotype when the user selects analysing a supervised data set, wherein linear discriminant analysis is used and the appropriate number of principal components is determined via a cross-validation function if more than two controls are used for each group and wherein nearest neighbour classification of samples into groups which is proceeded by a re-computation using linear discriminant analysis is used if one single control activates."],"number":20,"annotation":false,"title":false,"claim":true},{"lines":["An apparatus when implementing the method of any of claims 1 to 15."],"number":21,"annotation":false,"title":false,"claim":true}]}},"filters":{"npl":[],"notNpl":[],"applicant":[],"notApplicant":[],"inventor":[],"notInventor":[],"owner":[],"notOwner":[],"tags":[],"dates":[],"types":[],"notTypes":[],"j":[],"notJ":[],"fj":[],"notFj":[],"classIpcr":[],"notClassIpcr":[],"classNat":[],"notClassNat":[],"classCpc":[],"notClassCpc":[],"so":[],"notSo":[],"sat":[]},"sequenceFilters":{"s":"SEQIDNO","d":"ASCENDING","p":0,"n":10,"sp":[],"si":[],"len":[],"t":[],"loc":[]}}