ライフサイエンスコーパス: significance analysis of microarray

1 housekeeping genes, data were analyzed using Significance Analysis of Microarrays.

2 onses were compared among ABL tertiles using significance analysis of microarrays.

3 size, or mixture distribution of noises than Significance Analysis of Microarrays.

4 ble than those obtained by t-test P value or Significance Analysis of Microarrays.

5 alysis of microarrays and cell type-specific significance analysis of microarrays.

6 Through significance analysis of microarrays, 52 genes involved

7 Data were analyzed using the significance analysis of microarrays algorithm, the pred

8 The significance analysis of microarrays and a novel rank co

9 mpared with standard metrics, including both significance analysis of microarrays and cell type-speci

10 rays (Affymetrix) and analyzed the data with significance analysis of microarrays and prediction anal

11 hat identify differentially expressed genes (significance analysis of microarrays) and minimal subset

12 Multiclass significance analysis of microarrays comparing normal sk

13 We describe cell type-specific significance analysis of microarrays (csSAM) for analyzi

14 red analysis of global gene expression using significance analysis of microarrays detected 48 up-regu

15 contrast, standard analytical tools, such as significance analysis of microarrays, detected a marker

16 ayesian analysis tool was more accurate than significance analysis of microarrays for predicting chec

17 ved in these and our previous experiments by significance analysis of microarrays indicated excellent

18 iation sensitivity as a continuous variable, significance analysis of microarrays is used for gene se

19 ared with their corresponding controls using significance analysis of microarrays (<1% false discover

20 stic by either incorporating the idea of the significance analysis of microarrays method or using the

21 Use of significance analysis of microarrays methodology identif

22 crapings using the dCHIP software as well as Significance Analysis of Microarrays or SAM.

23 We explore the possibility of applying the Significance Analysis of Microarray (SAM) method (PNAS 9

24 cluding the empirical Bayes (EB) method, the significance analysis of microarray (SAM) method and the

25 cal Bayes method of Efron et al. (2001), the significance analysis of microarray (SAM) method of Tush

26 ical Bayesian method of Efron et al. and the Significance Analysis of Microarray (SAM) method of Tush

27 her level in the flower than the leaf by the Significance Analysis of Microarray (SAM) method with a

28 f KBD patients and healthy controls, through Significance Analysis of Microarray (SAM) software.

29 Significance Analysis of Microarray (SAM) was then utili

30 Assessment of the data by Significance Analysis of Microarrays (SAM) and cluster a

31 ession between the five different genotypes, significance analysis of microarrays (SAM) and one-way A

32 rray signal intensities were analyzed by the significance analysis of microarrays (SAM) approach.

33 Significance analysis of microarrays (SAM) is a widely u

34 identified by univariate approaches such as Significance Analysis of Microarrays (SAM) or Linear Mod

35 ctiveness of the proposed procedure with the significance analysis of microarrays (SAM) procedure.

36 The microarray datasets were analyzed with significance analysis of microarrays (SAM) to identify g

37 he bioinformatics software Cluster/TreeView, Significance Analysis of Microarrays (SAM), and ANNs.

38 We describe a method, Significance Analysis of Microarrays (SAM), that assigns

39 on Affymetrix GeneChips were analyzed using Significance Analysis of Microarrays (SAM), to determine

40 normal and malignant lesions, as defined by significance analysis of microarrays (SAM), were compare

41 dividuals and a rigorous statistical method, Significance Analysis of Microarrays (SAM).

42 Supervised analysis with Significance Analysis of Microarrays software between th

43 ates and stringent analytic tools, including significance analysis of microarrays to estimate and man

44 Significance analysis of microarrays was then used to cr

45 lusters of reactivity, and after unblinding, significance analysis of microarrays was used to identif

46 erential gene expression was assessed by the significance analysis of microarrays, with the false-dis