ライフサイエンスコーパス: decision tree

1 issociation, electron transfer dissociation, decision tree).

2 oftware was used to construct a risk-benefit decision tree.

3 esponds to the implementation of a molecular decision tree.

4 uture sequences; instead, one must prune the decision tree.

5 as values of individual branching steps in a decision tree.

6 ed in 2 formats, a standard "x/y" list and a decision tree.

7 hical decision making in a task with a small decision tree.

8 based on sequential detection of SNPs and a decision tree.

9 cally accumulates upon advancing through the decision tree.

10 d number of teeth identified AMI in the CART decision trees.

11 interlinked web pages, online databases, and decision trees.

12 ve methods significantly improve accuracy of decision trees.

13 performing a formal decision analysis using decision trees.

14 l user interface for building and evaluating decision trees.

15 ng regression based on conditional-inference decision trees.

16 n performance of (0.92), followed by SVM and decision trees (0.90).

17 Decision trees, a machine learning method, were trained

18 RPA was used to create a decision tree according to predictive variables that cla

19 ate methods backpropagation neural networks, decision tree, adaboost with decision tree, and support

20 We tested the resulting data-dependent decision tree against collision energy-optimized single

21 s, we designed and embedded a data-dependent decision tree algorithm (DT) to make unsupervised, real-

22 ared the prediction performances of a single decision tree algorithm C4.5 with six different decision

23 method, a pattern recognition algorithm (the decision tree algorithm C4.5) is then used to construct

24 A decision tree algorithm identified classifiers for respo

25 A decision tree algorithm is introduced to select and appl

26 Our machine learning approach based on a decision tree algorithm successfully provided several se

27 ces from the UniProt database and employed a decision tree algorithm to identify the sequence determi

28 Self-Organizing Global Ranking algorithm, a decision tree algorithm, and a support vector machine al

29 MediBoost significantly outperformed current decision tree algorithms in 11 out of 13 problems, givin

30 Current decision tree algorithms, however, are consistently outp

31 er, with commonly used association rules and decision tree algorithms.

32 nce-based attributes of the graph to train a decision tree allowed us to increase accuracy of SNP cal

33 Standardized decision tree analysis and Markov transitional models we

34 Furthermore, the techniques utilized for decision tree analysis have broad range of applicability

35 olar screening hit, PD012527, use of Topliss decision tree analysis led to the discovery of the nanom

36 In decision tree analysis, patient age was identified as an

37 These results show, through rigorous decision tree analysis, the potential cost-effectiveness

38 These results show through rigorous decision tree analysis, the potential cost-effectiveness

39 performance in genotype classification in a decision tree analysis.

40 Decision-tree analysis identified that the most importan

41 Decision-tree analysis showed that the stronger predicto

42 functional imaging with PET/CT and recursive decision-tree analysis to combine measurements of tumor

43 We used a decision tree and Markov model to estimate the cost-effe

44 RBF Neural Nets, MLP Neural Nets, Bayesian, Decision Tree and Random Forrest methods have been used

45 RBF Neural Nets, MLP Neural Nets, Bayesian, Decision Tree and Random Forrest methods.

46 d the relationships among GO attributes with decision trees and Bayesian networks, using the annotati

47 iBoost's performance to that of conventional decision trees and ensemble methods on 13 medical classi

48 eviously-described genefinder which utilizes decision trees and Interpolated Markov Models (IMMs).

49 First, we use tree-based analyses (decision trees and random forest algorithms) to discover

50 ere, two different machine-learning methods, decision trees and support vector machines (SVMs), are a

51 ormed standard methods (logistic regression, decision trees and support vector machines).

52 ort vector machines, hidden Markov model and decision tree) and developed an algorithm (SpiderP) for

53 eural networks, decision tree, adaboost with decision tree, and support vector machine, which interro

54 ard machine-learning algorithms-naive Bayes, decision trees, and artificial neural networks-can be ap

55 Results: We apply a decision tree approach to analyze the relationship of ce

56 based on a simple exclusion mechanism and a decision tree approach using the C4.5 data-mining algori

57 etting, this study shows that the use of the decision tree approach with the option of substituting c

58 We present a decision-tree approach to classify pseudogenes based on

59 Decision trees are built and evaluated based on a librar

60 By doing so, non-orthogonal decision trees are constructed using the selected templa

61 Decision trees are interpretable and are therefore used

62 These characteristics and decision trees are made available to facilitate alternat

63 Genetic testing decision trees are therefore broadly based on a small nu

64 ilable treatment modalities and to provide a decision tree as a general guide for clinicians to aid i

65 ox methods including logistic regression and decision trees as well as less interpreitable techniques

66 work using machine learning method, in which decision tree based classifier ensembles coupled with fe

67 ome by a novel mathematical approach using a decision tree based on genes ranked by increasing varian

68 The authors used decision trees based on theoretical models to assess the

69 ision tree algorithm C4.5 with six different decision-tree based classifier ensembles (Random forest,

70 vaccination program in the United States, a decision tree-based analysis was conducted with populati

71 ction results indicate that our (non-linear) decision tree-based classifier can predict operons in a

72 This decision-tree-based analysis was conducted to evaluate t

73 A cohort-based, decision tree, budget impact model was developed to esti

74 g three types of machine-learning technique: decision trees (C4.5), Naive Bayes and Learning Classifi

75 but uses a non-parametric procedure based on decision trees (called 'jump trees') to reconstruct the

76 Importantly, a decision tree can be used to classify sequences accordin

77 Our findings suggest that a clinical decision tree can be used to estimate a bacteremic patie

78 With simple variables, clinical decision trees can be used to distinguish patients at lo

79 The decision trees can be used to model various features of

80 iscriminant analysis, 3-nearest-neighbor and decision trees (CART)-using both synthetic and real brea

81 Decision tree chance node probabilities were estimated w

82 healthy men were used to train and develop a decision tree classification algorithm that used a nine-

83 The decision trees classified the data with 76.86% to 88.5%

84 iminating rules generated with the automated decision tree classifier allowed for discrimination betw

85 nsemble classifiers always outperform single decision tree classifier in having greater accuracies an

86 We apply this approach using a decision tree classifier to the genotype data of seven c

87 , by using the enriched rules in alternating decision tree classifiers, we are able to determine the

88 phenotypes with suspicion of CJD based on a decision tree combining CSF biomarkers.

89 baseline, RA prevalence was higher using the decision tree compared with the list approach (63% versu

90 This study evaluated the performance of the decision tree compared with the list approach in the asc

91 The final decision tree comprised 4 decision nodes and 5 terminal

92 The population entering the decision trees consisted of patients with CRC who had un

93 A modest search of decision trees containing at most seven decision nodes f

94 n of redundant sequences; (ii) clustering by decision trees coupled with analysis of ClustalW alignme

95 Summary tables and a decision tree demonstrate how these data can be compiled

96 -CID/ETD workflow combines the best possible decision tree dependent MS(2) data acquisition modes cur

97 A modified WHO decision-tree designed to detect high-risk asymptomatic

98 This article describes a decision tree developed to assist public health workers

99 Furthermore, the decision trees developed can be used to model various fe

100 A decision tree diagram to assist the practitioner in maki

101 racy of two fingerprint-based classifiers, a decision tree (DT) algorithm and a medoid classification

102 In this study, Decision Trees (DT) based models were developed to discr

103 mples, for which we propose a data dependent decision tree, else HCD is the method of choice.

104 hod is applied for the purpose of deriving a decision tree for decision-making.

105 als and coral reef ecosystems, and propose a decision tree for incorporating assisted evolution into

106 This review also describes a decision tree for the diagnosis and follow-up for ARCH i

107 retention time shifts was implemented, and a decision tree for validation is presented.

108 novel method for constructing non-orthogonal decision trees for mining protease data.

109 For the high complexity sample the decision tree gave the highest number of identified cros

110 The calibrated decision tree had the following test characteristics in

111 Decision trees have been applied to problems such as ass

112 show better generalization performance, but decision trees have the advantage of generating interpre

113 Multivariate analysis using a decision tree identified positive cytology as the most i

114 A modified decision-tree identified 6% of asymptomatic child contac

115 hod consistently improves the performance of decision trees in predicting peptide-MHC class I binding

116 The decision tree included a first-level decision node based

117 The decision tree included a Markov model with five states,

118 The derived decision tree included five candidate biomarkers, admiss

119 Decision-tree induction and the simple approach of using

120 A step-by-step decision tree is provided to allow clinicians to triage

121 The major strength of the decision tree is that it can take any measured feature a

122 ine, and outperforms other learning methods (decision trees, k-nearest neighbour and naive Bayes).

123 ing set is provided to an adaptively boosted decision tree learner to create a classifier for predict

124 e phase) was investigated in a pragmatic and decision tree-like performance evaluation strategy.

125 ian and outperformed other learning methods (decision trees, linear discriminate analysis, and k-near

126 The decision tree logic enabled by inSeq promises to circumv

127 Using inSeq and its advanced decision tree logic, we demonstrate (i) real-time predic

128 The alternating decision tree machine learning algorithm was applied for

129 of view were used to develop and validate a decision-tree machine-learning model.

130 We developed a hybrid decision tree/Markov model to simulate the costs, effect

131 ring the training and feature selection, the decision tree method achieves 82.6% overall accuracy wit

132 Using the selected features, a decision tree method can achieve 82.6% overall accuracy

133 we conclude that decision tree methodology may facilitate elucidation of

134 neural net approach and also in contrast to decision tree methods described recently, the pharmacoph

135 Similar to decision tree methods, the pharmacophore point filter pr

136 Data for our decision tree model came from men in the two arms (finas

137 A decision tree model of CBD exploration was developed to

138 70% overall classification accuracy and the decision tree model reveals insights concerning the comb

139 We constructed a decision tree model that compared the test option (scree

140 We used a decision tree model to analyse the costs of preventive m

141 We used an analytic decision tree model to compare the cost-effectiveness of

142 A cost-effectiveness decision tree model was constructed to analyze the cost-

143 A decision tree model was constructed to compare the cost-

144 A decision tree model was constructed to reflect progressi

145 A decision tree model was constructed using probabilities,

146 A decision tree model was used to simulate an influenza va

147 ss of the additional use of (18)F-FET PET, a decision tree model was used.

148 und to be a cheaper diagnostic strategy in a decision tree model when United Kingdom-based costs were

149 ir levels of enzymatic activity based on the decision tree model which showed GPX and total protein a

150 We developed a decision tree model with a lifetime horizon.

151 The decision tree model with the highest overall accuracy an

152 Furthermore, using a random forests decision tree model, eight out of 10 stress conditions w

153 With a decision tree model, key structural features required fo

154 A 3-arm decision-tree model was developed that employs standard

155 A decision-tree model was set up to compare 2 strategies:

156 elected cytokines were used to build a final decision-tree model.

157 Machine learning optimization resulted in a decision tree modeling that predicted T2D incidence with

158 ity in emergency admissions, using empirical decision Tree models because they are intuitive and may

159 rtitioning was performed to evaluate various decision tree models by using the CT signs.

160 Decision tree models developed on the entire Gag-plus-PR

161 opic Roux-en-Y gastric bypass and to develop decision tree models to optimize diagnostic accuracy.

162 The workflow was process mapped, decision tree models were constructed using probabilitie

163 On the basis of these classifications, decision tree models were generated using the molecular

164 A hierarchical decision-tree MS(n) method is used in conjunction with a

165 We used five statistical learning methods (decision trees, neural networks, support vector regressi

166 he samples were classified using the oblique decision tree (OC1) algorithm to provide a procedure for

167 tioning analysis (RPA), a method of building decision trees of significant prognostic factors for out

168 solve subtasks, and maladaptively prune the decision trees of subtasks in a reflexive manner upon en

169 lity was obtained with either random forest, decision tree or logistic regression analysis.

170 Decision-tree partitioning algorithms are used to isolat

171 A decision tree performs better than existing methods when

172 ression tree analysis was used to generate a decision tree predicting 28-day mortality based on a com

173 The decision trees produced are intelligible and can be used

174 Decision tree provides a path to diagnose and treat lesi

175 Logistic regression, decision trees, random forests, and multivariate adaptiv

176 ulation of single cells using a data-derived decision tree representation that can be applied to cell

177 The decision tree's positive and negative predictive values

178 The constructed decision trees select about 10 out of 50,000 k-mers.

179 of support vector machines, neural networks, decision trees, self-organizing feature maps (SOFM) and

180 Thus, we have used decision tree sensitivity analysis to assess the cost-ef

181 The CT + PET strategy in the conservative decision tree showed a saving of $1154 per patient witho

182 e CT + PET strategy in the less conservative decision tree showed a savings of $2267 per patient but

183 nces, and secondary structural features in a decision tree that categorizes mRNAs into those with pot

184 ntation of the discriminant functions into a decision tree that constitutes a new program called Firs

185 ursive partitioning techniques to generate a decision tree that defines the gating strategy.

186 ur current understanding of those tests, the decision tree that is involved in this process, and an e

187 Using this database we trained a decision tree that shows the order of importance, and ra

188 dicted with a high predictive accuracy using decision trees that include four to six readily attainab

189 ediBoost, a novel framework for constructing decision trees that retain interpretability while having

190 of an established machine learning method, a decision tree, that can rigorously classify sequences.

191 We use the decision tree to classify de novo assembled sequences an

192 se metrics are used to create a RandomForest decision tree to classify the sequencing data, and PARTI

193 ecursive partitioning was used to generate a decision tree to determine the likelihood that a bactere

194 in conjunction with a testing and treatment decision tree to estimate the cost-effectiveness of birt

195 An Expert System (ES) - a computerised decision tree to guide behaviour change - was developed

196 s and different genetic causes, we propose a decision tree to guide clinical genetic testing in patie

197 herapeutic challenges are highlighted, and a decision tree to guide treatment in patients with early

198 We developed a decision tree to identify VR-PAH patients on the basis o

199 annotation comparison results, we designed a decision tree to obtain a consensus functional annotatio

200 alysis algorithms along with a sophisticated decision tree to place reads into "best fit" functional/

201 Next, we built a decision tree to predict the presence of EGFR and KRAS m

202 Our objective was to develop a user-friendly decision tree to predict which organisms are ESBL produc

203 HE score are steps toward the development of decision trees to define EoE subpopulations and, consequ

204 We used decision trees to estimate the probability of actions ta

205 we introduce a domain-based random forest of decision trees to infer protein interactions.

206 charomyces cerevisiae by using probabilistic decision trees to integrate multiple types of data, incl

207 We use decision trees to predict phenotypes associated with Sac

208 Inductive Logic Programming data mining, and decision trees to produce prediction rules for functiona

209 the findings of this review into a pragmatic decision tree, to guide the further management of the in

210 n of a particular machine learning approach, decision trees, to the tasks of predicting a protein's s

211 and regression tree analysis, a data-mining decision tree tool analysis.

212 A decision-tree tool was developed to investigate the impa

213 as random forests, which relies on a set of decision trees trained using length, sequence and other

214 A decision tree used estimates of disease burden, costs, v

215 The final decision tree used four variables: emphysema, airway abn

216 The resulting trees are of the same type as decision trees used throughout clinical practice but hav

217 This study aimed at creating a decision tree using clinical characteristics, serum tryp

218 riteria were applied cumulatively, while the decision tree was applied cross-sectionally using either

219 The first decision tree was conservatively constructed by requirin

220 A decision tree was constructed to compare a strategy of l

221 A decision tree was constructed to determine short-term "i

222 A two-level decision tree was designed, and a multivariate logistic

223 A recursive decision tree was developed in which the imaging investi

224 A decision tree was developed to compare the treatment str

225 d accuracy (95% confidence intervals) of the decision tree were 82.4% (63.9%-93.9%), 0% (0%-10.4%), a

226 Seven decision trees were built with the use of expression lev

227 Several decision trees were developed and were then tested using

228 Clinical decision trees were developed using the recursive partit

229 chine (SVM), k-Nearest Neighbors (k-NN), and Decision Tree, were employed to study the sophisticated

230 tion accuracies compared to that of a single decision tree when applied to a pancreatic cancer proteo

231 In this economic evaluation, we combined a decision tree with a Markov state transition model to co

232 We used a decision tree with sensitivity analyses to determine the

233 Conclusion The decision tree with the highest accuracy and sensitivity

234 The decision tree with the highest specificity included SMV

235 ecision nodes finds a large number of unique decision trees with an average sensitivity and specifici

236 The use of the decision trees would decrease the number of contacts inv