ライフサイエンスコーパス: data interpretation

1 Data interpretation demonstrated the efficacy of delta(18)O a

2 Data interpretation from assays such as ChIA-PET and Hi-C is

3 Data interpretation is complicated by the fact that short seq

4 Data interpretation through pattern recognition methods (PCA,

5 on AHRQ's recommendations and compiled under 3 domains: (1) data interpretation (interpreting data as hospitalization rec

6 (HJC) within the pelvis is thus critical to ensure accurate data interpretation.

7 s, as the inclusion of compromised cells inevitably affects data interpretation.

8 es ribonuclease digestion followed by LC-MS/MS analysis and data interpretation.

9 Vapor intrusion (VI) pathway assessment and data interpretation have been guided by an historical concept

10 Serious problems in the experimental design and data interpretation raise concerns about the validity of the

11 in relation to patient variability, experimental design and data interpretation.

12 main elusive, which may complicate experimental designs and data interpretation.

13 sequencing analysis is necessary for library generation and data interpretation.

14 multaneous quantitative mapping of multiple parameters, and data interpretation and analysis.

15 ized steps enhance the uniformity of clinical practices and data interpretation.

16 efficient and effective biclustering methods for biological data interpretation.

17 ls, which provide more convincing, objective, and completed data interpretation.

18 As a consequence, data interpretation increases in complexity.

19 urrent drawbacks of this model is essential for the correct data interpretation and extrapolation of conclusions applicab

20 at are informative to device design as well as experimental data interpretation.

21 ries data requires appropriate computational algorithms for data interpretation.

22 A popular approach for data interpretation is the determination of the binding affin

23 esistance catalogues that the respective tools employed for data interpretation.

24 ce sensitivities remains largely unexplored, especially for data interpretation.

25 Visualization is essential for data interpretation, hypothesis formulation and communication

26 shot to shot is not only challenging but also important for data interpretation.

27 ime using a portable reader which removed subjectivity from data interpretation.

28 respect to the gene (gene area) is important for functional data interpretation; hence algorithms that match regions to g

29 ied to help overcome the ever-growing challenges of genetic data interpretation.

30 th establishing PCP in the vestibule are unclear, hindering data interpretation and employment of the vestibule for PCP s

31 examine several published studies where confusion arose in data interpretation, to illustrate the challenges.

32 EE) and handgrip strength provided a valuable assessment in data interpretation of body composition.

33 f modern sequencers present new computational challenges in data interpretation, including mapping and de novo assembly.

34 We consider potential pitfalls in data interpretation and place particular emphasis on recent s

35 ical model validation that, in turn, can lead to inaccurate data interpretation.

36 which was in Ebola virus expression, was based on incorrect data interpretation.

37 sics even though our ignorance about the HX mechanism makes data interpretation imprecise.

38 Often, manual data interpretation is required and our knowledge of the expe

39 However, NGS data interpretation is associated with challenges that must b

40 New studies are addressing challenges related to NHANES data interpretation in health risk contexts.

41 hat signal a potential paradigm shift from conventional NMR data interpretation, which may be of particular utility for c

42 arch and the biopharmaceutical industry, the development of data interpretation methods is lagging behind.

43 nanoparticle formulations, including a short commentary on data interpretation and translation.

44 ted drug concentrations and confounded pharmacokinetic (PK) data interpretation.

45 In view of overall study quality, data interpretation should be cautious, but high mortality an

46 ion of this schema was undertaken in an attempt to simplify data interpretation at a time when the ontogeny and functiona

47 nd reducing changes that would alter samples and subsequent data interpretation.

48 in analysis time) and challenges (suitable solvent systems, data interpretation) of the approach.

49 ted in the past few years, which poses a great challenge to data interpretation.

50 RTM may affect clinical trial data interpretation when the outcome measure has high variabi