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1                                                             Data interpretation of desorption tests yields an in situ K(d
2                                                           A data interpretation and processing approach for improved comp
3 or data resolution in t-SNE space and thereby more accurate data interpretation.
4 by the secondary digestion for MS has made the analysis and data interpretation complicated.
5             Serious problems in the experimental design and data interpretation raise concerns about the validity of the
6 elines for the design of the sensing, signal detection, and data interpretation methods to avoid this constraint, which r
7  however, SIRM experiments can be difficult to execute, and data interpretation is challenging.
8  has become essential for quality control, exploration, and data interpretation.
9 ntal design, sample multiplexing, batch effect removal, and data interpretation.
10                                               By automating data interpretation, GlycanAnalyzer enables the easier use of
11 y in size, configuration and cellular location, challenging data interpretation in causation studies.
12 of isobaric and isomeric species, which greatly complicates data interpretation when only m/z information is available.
13 he absence of the organ level adaptions that often confound data interpretation in germline transgenic model organisms.
14 s of experimental measurements is a must to achieve correct data interpretation.
15 adduct decay should be known first in bulk oils for correct data interpretation in DMOs.
16 CYP3A4) and human aldehyde oxidase (hAOX) for more in-depth data interpretation, and both enzymes resulted in active PROT
17 oach and whether triangulation across methods could enhance data interpretation.
18 , including use of covariate stability, can greatly enhance data interpretation and confidence in variable selection.
19 re studies it would be of interest to investigate how final data interpretation is influenced by different processing sof
20 nel, including REG1B instead of REG1A, and an algorithm for data interpretation, the PancRISK score, in additional retros
21  assayed and robust statistical approaches were applied for data interpretation.
22                                      A popular approach for data interpretation is the determination of the binding affin
23 nformatic tools and field-specific biological expertise for data interpretation.
24 l approaches, such as pathway enrichment, are important for data interpretation at ultra-low input.
25                           Descriptive analysis was used for data interpretation.
26                                      The methodology guided data interpretation and collective analyses confirming how to
27                                                    However, data interpretation is challenged by the complexity of origin
28  experiments confirmed that this was insufficient to impact data interpretation.
29  in individual risk leads to substantial biases that impair data interpretation and policy decisions.
30 ntal controls and measures of cross-validation that improve data interpretation.
31  extracellular flux analyses integrate with SIRM to improve data interpretation.
32 rstanding the patterns behind these influences will improve data interpretation and lead to the development of new climat
33     Our observations, additionally, suggest that caution in data interpretation is warranted when using the CD4Cre transg
34 ncer immunotherapy, and discuss important considerations in data interpretation and current technological limitations.
35 tion stated explicitly, which could lead to difficulties in data interpretation and downstream analyses.
36  terms, minimize biases introduced by redundant GO terms in data interpretation, and batch processing of multiple GO enri
37 quantity of data produced by NTS analytical workflows makes data interpretation and time-dependent monitoring of samples
38 to branching residues was established to simplify the MS/MS data interpretation of closely related isomeric structures.
39 quencing data on a per-individual basis that transforms NGS data interpretation from variant-level to gene-level.
40        Through these descriptions, we demonstrate that OCTA data interpretation can be ambiguous if performed without con
41  post-translational glycan modifications, and complexity of data interpretation.
42 y, and 2) classifying study findings according to degree of data interpretation.
43 cancer trials, resulting in inconsistency and difficulty of data interpretation.
44 es all the associated visualization plots, to allow ease of data interpretation and manuscript preparation.
45 e execution of individual analysis types, provide advice on data interpretation and make the complete code available onli
46 o address the growing and unique need for phosphoproteomics data interpretation, we have implemented phosphosite set anal
47  and Laboratory Standards Institute guidelines for sequence data interpretation.
48 Such high-dimensional molecular profiles pose challenges to data interpretation and hypothesis generation.
49 ed workflow for CIU experiments, from sample preparation to data interpretation using online size exclusion chromatograph
50  Finally, we demonstrate the application of the DE prior to data interpretation in three use cases: (i) breast cancer sub