ライフサイエンスコーパス: classification tree

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 rank regression (RRR) and random forest with classification tree analysis (RF-CTA).

14 Hierarchically optimal classification tree analysis identified an ordered five-

15 Classification tree analysis of lic2B, hmwA, and the nin

16 Classification tree analysis of salivary microbiological

17 Classification tree analysis revealed that a threshold o

18 Classification tree analysis showed that the highest ris

19 Classification tree analysis was performed to identify h

20 Using classification tree analysis, two (or three) specific SS

21 ation rules for African Americans based on a classification tree and a logistic regression model.

22 When we used the classification tree and random forest supervised classif

23 Importantly, combined results of the classification tree and the ANN analyses provided highly

24 Classification tree and the binary logistic regression m

25 a deterministic procedure to form forests of classification trees and compare their performance with

26 parison, we introduce a methodology based on classification trees and demonstrate that it is signific

27 Two multivariable analyses, classification trees and polychotomous logistic regressi

28 Classification trees and tree-structured survival analys

29 Logistic regression, classification tree, and mixture discriminant analysis (

30 d subsets of predictors to create individual classification trees, and this process is repeated to ge

31 In addition, competing classification trees are displayed, which suggest that d

32 The classification tree built using the breast cancer data s

33 Classification trees can be used to determine which visu

34 thods, including artificial neural networks, classification trees, discriminant analysis, k-Nearest n

35 We also developed a classification tree for identification of individuals wh

36 The final classification tree for predicting ESBL-positive bactere

37 A new method for constructing classification trees, for which the branches comprise SV

38 The classification tree had a sensitivity of 100% (95% confi

39 The classification tree had a similar expected prediction er

40 The SEER-based classification tree identified additional criteria to ex

41 The classification tree identified males over the age of 27

42 A classification tree identified pain at rest with a score

43 Classification trees identified previous hospitalization

44 ession model, artificial neural network, and classification tree, in predicting advisories due to FIB

45 A classification tree is generated using machine learning

46 The classification tree method revealed IL-6 and CRP as the

47 Furthermore, multivariate classification tree model analysis showed that stage and

48 We trained a hierarchical classification tree model on publicly available transcri

49 ledge and these newly discovered features, a classification tree model was built to predict genome-wi

50 early glaucoma, were used as predictors in a classification tree model.

51 Classification tree models suggest that patterns of base

52 c modeling framework is based on statistical classification tree models that evaluate the contributio

53 In the classification tree models, the results showed that when

54 lve the class prediction problem, we built a classification tree on the learning set, and then sought

55 The classification trees performed similarly to proportional

56 The proposed classification tree permitted correct classification of

57 ship on the data, with branch lengths in the classification tree representing the degree of separatio

58 ere identified that, when combined through a classification tree signature, accurately classified pat

59 lan-Meier recovery curves and a multivariate Classification Tree Structure Survival Analysis were per

60 Two-step classification tree suggested that homogeneous high T1 S

61 ally construct a much simplified topology, a classification tree, suggested by the ARG.As the test ca

62 st neighbor classifier, bagging and boosting classification trees, support vector machine, and random

63 of this bioinformatic analysis implements a classification tree that evaluated 5'-UTRs for unique co

64 artitioning was used to develop a SEER-based classification tree that was validated using PLG data.

65 eins were used to generate multiple decision classification trees to distinguish the known disease st

66 Furthermore, we apply classification trees to relate injury structure to the b

67 We used recursive partitioning (classification trees) to derive an algorithm based on he

68 We validated our classification tree using a subset of 222 participants n

69 We have developed a classification tree using clinical and radiographic data

70 A classification tree using these two features has a cross

71 Next, a new PLG-based classification tree was developed using the expanded set

72 The classification tree was developed with a sensitivity and

73 Classification trees were constructed to verify the stre

74 Classification trees were used to identify characteristi

75 We fit the 87 samples of the first set to a classification tree, which neatly separated into four ma