ライフサイエンスコーパス: decision tree

1 issociation, electron transfer dissociation, decision tree).

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

3 cally accumulates upon advancing through the decision tree.

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

5 esponds to the implementation of a molecular decision tree.

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

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

8 ression, random forest, and gradient boosted decision tree.

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

10 omographic data were analyzed using Smadja's decision tree.

11 P) for nonmalignant lesions was modeled in a decision tree.

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

13 l user interface for building and evaluating decision trees.

14 ng regression based on conditional-inference decision trees.

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

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

17 ve methods significantly improve accuracy of decision trees.

18 PDM was developed using decision trees.

19 performing a formal decision analysis using decision trees.

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

21 results obtained report 0.87 on accuracy by decision tree, 0.96 by random forest, 0.91 by simple neu

22 Decision trees, a machine learning method, were trained

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

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

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

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

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

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

29 A decision tree algorithm identified 3 metabolites that co

30 A decision tree algorithm identified classifiers for respo

31 A decision tree algorithm is introduced to select and appl

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

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

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

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

36 Current decision tree algorithms, however, are consistently outp

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

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

39 Decision tree analyses confirmed the accuracy and robust

40 Standardized decision tree analysis and Markov transitional models we

41 Decision tree analysis identified hemoglobin less than 8

42 Within the limitations of this study, CHAID decision tree analysis identified the most plausible ris

43 CHAID decision tree analysis identified three predictors (hist

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

45 g concentrations of biomolecules followed by decision tree analysis reveal that the highly differenti

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

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

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

49 performance in genotype classification in a decision tree analysis.

50 robability of remission was modeled by using decision tree analysis.

51 Decision-tree analysis identified hemoglobin <8.5 g/dL a

52 Decision-tree analysis identified that the most importan

53 Decision-tree analysis showed that the stronger predicto

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

55 Applying decision-tree analysis to our measurements, we are able

56 ilor-made, experts' feedback using an online decision tree and database introduced here.

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

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

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

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

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

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

63 We used linked decision trees and Markov models to evaluate outcomes sh

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

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

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

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

68 ethods such as convolutional neural network, decision tree, and eXtreme gradient boosting as well as

69 Vector Machines (SVM), deep neural network, decision tree, and Random Forest (RF), using a k-fold cr

70 ted learning technique for creating a single decision tree, and shows that this method can produce mo

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

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

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

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

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

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

77 A decision-tree approach was used to identify peptides com

78 Decision trees are built and evaluated based on a librar

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

80 Decision trees are interpretable and are therefore used

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

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

83 that of standard supervised methods, namely: decision tree, artificial neural network, support vector

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

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

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

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

88 lassifiers: regularised logistic regression, decision trees based on classification and regression tr

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

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

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

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

93 We sought to apply decision tree-based methods, C5.0 and logic regression,

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

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

96 radient boosting can improve the accuracy of decision trees, but at the expense of the interpretabili

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

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

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

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

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

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

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

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

105 other methods by introducing more levels of decision tree classification with inputs from the same m

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

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

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

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

110 sed on their time-resolved roundness using a decision tree classifier.

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

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

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

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

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

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

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

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

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

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

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

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

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

124 A decision tree diagram to assist the practitioner in maki

125 Our decision tree differentiated between serum specimens fro

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

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

128 ar forecasting model based on a logic matrix decision tree enabled an analysis of surgeon productivit

129 opose VEF, a variant filtering tool based on decision tree ensemble methods that overcomes the main d

130 We utilised the results to construct a decision tree for assessing whether an AD meta-analysis

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

132 Net cost per patient was estimated from the decision tree for each strategy.

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

134 round of the Delphi method, we established a decision tree for oligometastatic disease classification

135 We provide a decision tree for researchers to help make monitoring de

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

137 rican populations and suggests a therapeutic decision tree for treatment of TNBC.

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

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

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

141 The calibrated decision tree had the following test characteristics in

142 Decision trees have been applied to problems such as ass

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

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

145 Decision trees identified the measure that could predict

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

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

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

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

150 The derived decision tree included five candidate biomarkers, admiss

151 A method for decision tree induction is presented.

152 Decision-tree induction and the simple approach of using

153 Each branch of the decision tree involves initial coactivation of bipotenti

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

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

156 .08 to 97.67), Naive Bayes (54.05 to 96.67), Decision tree J48 (67.57 to 97.00), and SMO_npolyk (59.4

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

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

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

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

161 The decision tree logic enabled by inSeq promises to circumv

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

163 The alternating decision tree machine learning algorithm was applied for

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

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

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

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

168 we conclude that decision tree methodology may facilitate elucidation of

169 This article introduces a decision-tree methodology that analyzes a patient's dile

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

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

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

173 A decision tree model of CBD exploration was developed to

174 h or low risk of disease specific death, our decision tree model reported that tumour budding was the

175 70% overall classification accuracy and the decision tree model reveals insights concerning the comb

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

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

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

179 We constructed a decision tree model to project lifetime costs and benefi

180 A decision tree model was analysed from a health provider

181 A decision tree model was constructed based on the probabi

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

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

184 A decision tree model was constructed to reflect progressi

185 A decision tree model was constructed using probabilities,

186 A decision tree model was used to compare costs and outcom

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

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

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

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

191 m a health-care provider perspective using a decision tree model with a lifetime horizon.

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

193 The decision tree model with the highest overall accuracy an

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

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

196 ildhood invasive pneumococcal disease, and a decision-tree model to predict a range of clinical prese

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

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

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

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

201 Hierarchic clustering and decision tree modeling were used to associate demographi

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

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

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

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

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

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

208 Among various survival decision-tree models, the modified Charlson Comorbidity

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

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

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

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

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

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

215 Decision-tree partitioning algorithms are used to isolat

216 A decision tree performs better than existing methods when

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

218 The decision trees produced are intelligible and can be used

219 Decision tree provides a path to diagnose and treat lesi

220 icroarray data (linear modeling) and Boruta (decision trees) R packages, with 55 being previously ide

221 machine learning (ML) algorithms, including decision tree, random forest, multi-layer perceptron neu

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

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

224 lications like medicine, it also can produce decision trees represented by hybrid models between CART

225 In the decision tree rollback analysis, a clinical effectivenes

226 In the decision tree rollback analysis, a clinical effectivenes

227 The decision tree's positive and negative predictive values

228 spective was conducted by combining a 5-year decision tree screening model with results from a Markov

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

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

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

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

233 A human-interpretable decision tree shows that phase selection is driven prima

234 In this economic model, we used a decision tree template to compare the intervention of a

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

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

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

238 Consortium for Top-Down Proteomics provide a decision tree that guides researchers to robust protocol

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

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

241 We built a decision tree that was 82.4% accurate, 100% (95% confide

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

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

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

245 This paper proposes a new three-level decision tree (TLDT) approach to map forest, shadowy, ba

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

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

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

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

250 We designed a decision tree to evaluate MMR vaccination at a pretravel

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

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

253 A decision tree to guide clinicians in classifying nodules

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

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

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

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

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

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

260 learning algorithm based on gradient boosted decision trees to analyze perspiration samples.

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

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

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

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

265 We use decision trees to predict phenotypes associated with Sac

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

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

268 ation and prediction), an ensemble method of decision trees, to predict tissue-specific cis-eQTL SNVs

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

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

271 The decision-tree tool presented could aid personalized tran

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

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

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

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

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

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

278 0.88 [95% CI 0.84-0.93] in gradient boosted decision tree vs 0.62 [95% CI 0.53-0.70] in reference mo

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

280 The decision tree was applied to a realistic patient profile

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

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

283 A decision tree was constructed with associated 2-year mor

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

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

286 A decision tree was developed to compare the treatment str

287 A decision tree was generated and incremental cost-utility

288 A Bayesian decision tree was used to estimate the probability (95%

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

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

291 Several decision trees were developed and were then tested using

292 Clinical decision trees were developed using the recursive partit

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

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

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

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

297 Conclusion The decision tree with the highest accuracy and sensitivity

298 The decision tree with the highest specificity included SMV

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

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