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1 n of the bromodomain family, visualized as a classification tree.
2 mal placement of each test sample within the classification tree.
3 rtality using logistic regression models and classification trees.
4 ults are visualized both as MDS plots and as classification trees.
5 procedure that is a generalization of single classification trees.
6  discriminant analysis, nearest neighbor and classification trees.
7 s, three-nearest-neighbor classification and classification trees.
8 tments with 83.3% (random forest) and 88.9% (classification tree) accuracy.
9                         In method 2, the ACR classification tree algorithm was applied.
10 re then entered into a risk analysis using a classification tree algorithm.
11                                We describe a classification-tree algorithm to guide studies of early
12                      Logistic regression and classification tree analyses were used to examine the ri
13                                        Using classification tree analyses, we found that low levels o
14 rank regression (RRR) and random forest with classification tree analysis (RF-CTA).
15                       Hierarchically optimal classification tree analysis identified an ordered five-
16                                              Classification tree analysis of lic2B, hmwA, and the nin
17                                              Classification tree analysis of salivary microbiological
18                                              Classification tree analysis revealed that a threshold o
19                                              Classification tree analysis showed that the highest ris
20                                      We used classification tree analysis to establish diagnostic cut
21                                              Classification tree analysis was performed to identify h
22                                        Using classification tree analysis, two (or three) specific SS
23 as identified via recursive partitioning and classification tree analysis.
24 ation rules for African Americans based on a classification tree and a logistic regression model.
25 fied potential risk factors using predictive classification tree and random forest ensemble models.
26                             When we used the classification tree and random forest supervised classif
27         Importantly, combined results of the classification tree and the ANN analyses provided highly
28                                              Classification tree and the binary logistic regression m
29 a deterministic procedure to form forests of classification trees and compare their performance with
30 parison, we introduce a methodology based on classification trees and demonstrate that it is signific
31 he traditional statistical analysis based on Classification trees and logistic models.
32                  Two multivariable analyses, classification trees and polychotomous logistic regressi
33                  We developed sex-stratified classification trees and random forests using 1,458 pred
34                                              Classification trees and tree-structured survival analys
35 Intelligence (AI)-methodology called optimal classification trees and validated for prediction of ES
36                         Logistic regression, classification tree, and mixture discriminant analysis (
37 d subsets of predictors to create individual classification trees, and this process is repeated to ge
38                       In addition, competing classification trees are displayed, which suggest that d
39                                          The classification tree built using the breast cancer data s
40                                              Classification trees can be used to determine which visu
41 thods, including artificial neural networks, classification trees, discriminant analysis, k-Nearest n
42 mpared to conventional penalized regression, classification trees, feed-forward neural network and a
43                          We also developed a classification tree for identification of individuals wh
44                                    The final classification tree for predicting ESBL-positive bactere
45 nuous variables, and logistic regression and classification trees for multivariable analysis that exa
46                A new method for constructing classification trees, for which the branches comprise SV
47                                  The optimal classification tree had 3 levels: maximum esophageal bod
48                                          The classification tree had a sensitivity of 100% (95% confi
49                                          The classification tree had a similar expected prediction er
50                               The SEER-based classification tree identified additional criteria to ex
51                                          The classification tree identified males over the age of 27
52                                            A classification tree identified pain at rest with a score
53                                              Classification trees identified previous hospitalization
54 ession model, artificial neural network, and classification tree, in predicting advisories due to FIB
55                                            A classification tree is generated using machine learning
56                                          The classification tree method revealed IL-6 and CRP as the
57 icity 86.0%, and accuracy 82.2%, whereas the classification tree model achieved a sensitivity of 84.2
58                    Furthermore, multivariate classification tree model analysis showed that stage and
59                    We trained a hierarchical classification tree model on publicly available transcri
60 ledge and these newly discovered features, a classification tree model was built to predict genome-wi
61 early glaucoma, were used as predictors in a classification tree model.
62 ght >2 cm at 5 minutes/impacted tablet and a classification tree model.
63 obstruction when using multiple metrics in a classification tree model.
64                                              Classification tree models suggest that patterns of base
65 c modeling framework is based on statistical classification tree models that evaluate the contributio
66                                       In the classification tree models, the results showed that when
67               Risk groups were developed via classification tree models.
68  interpretable AI methodology called optimal classification trees (OCTs) was applied in an 80:20 deri
69 lve the class prediction problem, we built a classification tree on the learning set, and then sought
70                                          The classification trees performed similarly to proportional
71                                 The proposed classification tree permitted correct classification of
72                                            A classification tree procedure was used to identify chara
73 ship on the data, with branch lengths in the classification tree representing the degree of separatio
74 ere identified that, when combined through a classification tree signature, accurately classified pat
75 lan-Meier recovery curves and a multivariate Classification Tree Structure Survival Analysis were per
76                                     Two-step classification tree suggested that homogeneous high T1 S
77 ally construct a much simplified topology, a classification tree, suggested by the ARG.As the test ca
78 st neighbor classifier, bagging and boosting classification trees, support vector machine, and random
79  of this bioinformatic analysis implements a classification tree that evaluated 5'-UTRs for unique co
80                   It utilizes a hierarchical classification tree that integrates three machine learni
81 onformal predictor applied to a hierarchical classification tree that was trained against the DART-HR
82 artitioning was used to develop a SEER-based classification tree that was validated using PLG data.
83 eins were used to generate multiple decision classification trees to distinguish the known disease st
84 cally distinct from those offshore, allowing classification trees to identify foraging habitats more
85                                  We used (1) classification trees to identify serum PCB congeners and
86                        Furthermore, we apply classification trees to relate injury structure to the b
87              We used recursive partitioning (classification trees) to derive an algorithm based on he
88 S-DA), support vector machine (SVM), and SVM classification tree type entropy (SVMTreeH).
89                             We validated our classification tree using a subset of 222 participants n
90                          We have developed a classification tree using clinical and radiographic data
91                                            A classification tree using these two features has a cross
92                        Next, a new PLG-based classification tree was developed using the expanded set
93 nd the turmeric-identification method) and a classification tree was developed using these data.
94                                          The classification tree was developed with a sensitivity and
95                                            A classification tree was fit to refine the rule and valid
96                                              Classification trees were constructed to verify the stre
97                                              Classification trees were used to identify characteristi
98                                              Classification trees were used to predict ischemic strok
99  We fit the 87 samples of the first set to a classification tree, which neatly separated into four ma