ライフサイエンスコーパス: used to identify

1 sions govern many important physiological processes and are used to identify cells for analysis and purifications.

2 on of coarse-grained "Martini" and all-atom simulations are used to identify two, to our knowledge, new cholesterol-bindi

3 Blood DC analysis can be used to identify ALS patients with an altered course of infla

4 At its current stage of development, the assay can be used to identify and characterize telomere length distributio

5 Visual rating scales can be used to identify distinct AD subtypes.

6 ur results demonstrate that CATH functional families can be used to identify drug-target interactions, opening a new rese

7 tutional variation in performance and patient volume can be used to identify high-priority areas for quality improvement

8 on of bacterial growth in 3-d, deviations from which can be used to identify phenotypic heterogeneities and inter-cellula

9 It can be used to identify the creep towards complete antibiotics resis

10 The advanced techniques of Deep Learning can be used to identify the significance of different features from

11 cific molecular signature of radiation sensitivity could be used to identify the optimum radiotherapy dose.

12 e to weight for having a low metabolic risk, which could be used to identify youths at risk.

13 This may be used to identify unique chemical signatures of various cells.

14 Next-generation sequencing (NGS) approaches are commonly used to identify key regulatory networks that drive transcrip

15 unsupervised clustering of gene expression data is commonly used to identify heterogeneity in biologic samples.

16 Seq data from Serratia marcescens transposon mutant library used to identify genes that contribute to fitness in a murine

17 nt of nuclear magnetic resonance (NMR) spectra is routinely used to identify and characterize molecules and molecular int

18 D A single-center continuous flow-LVAD database (n=354) was used to identify patients with chest computed tomographies pe

19 Multivariate Cox regression analysis was used to identify covariates associated with OS.

20 resonance (NMR) spectroscopy-based metabolomic analysis was used to identify metabolites in neonate cord serum associated

21 onal Readmission Database encompassing 722 US hospitals was used to identify index PCI cases in patients >/=18 years old.

22 Second, lesion network mapping was used to identify brain regions functionally connected to the

23 MaxQuant was used to identify peptides and proteins and quantify heavy/lig

24 A murine transgenerational asthma model was used to identify involved pathways.

25 st, the random effects output of the hierarchical model was used to identify outlier hospitals where the odds of delayed

26 A hierarchical logistic regression model was used to identify predictors of delayed fixation.

27 his cohort, a Cox proportional hazards regression model was used to identify predictors of survival.

28 A hierarchical switching state-space model was used to identify transiting and area-restricted search (ARS)

29 Multivariable logistic regression modelling was used to identify predictors of safe discharge, defined as the

30 Here, functional MRI was used to identify the network of brain regions that support WM

31 Site-directed mutagenesis of RadH was used to identify catalytic residues and provide insight into

32 ation cohort, and backward stepwise logistic regression was used to identify factors independently associated with CED.

33 Logistic regression was used to identify risk factors for new persistent opioid use.

34 ell lysate and purified target mRNA fused to a reporter was used to identify active zwitterions.

35 First, previously published qualitative research was used to identify important domains for the Population, Interv

36 Here, unbiased mass spectrometry was used to identify the microtubule affinity-regulating kinase f

37 TransDetect was used to identify novel TF modules regulating the expression o

38 Hospitalization data from 2003 to 2012 were used to identify the prevalence of associated risk factors fo

39 ional Surgical Quality Improvement Program (2006-2012) were used to identify females with invasive breast cancer undergoi

40 Univariate and multivariate regression analyses were used to identify independent predictors of poor specimen outc

41 LC-MS and multivariate/univariate statistical analysis were used to identify differences in rumen metabolites.

42 fractionation and bioactivity-guided chemical analysis were used to identify the bio-active ethyl acetate fraction.

43 Machine-learning approaches were used to identify relevant in vivo, ex vivo, and patient featu

44 dy) linked to the NSOC (National Study of Caregiving), were used to identify elderly stroke survivors and their caregiver

45 Immunohistochemistry and flow cytometry were used to identify myeloid cells and neuronal loss.

46 Population-based health services data were used to identify primary mental disorders during the 3 prior

47 Linear models with generalized estimating equations were used to identify risk factors for BCVA loss of 1 line or more

48 gene profiling, gene transduction, and immunohistology were used to identify molecules involved in DC subset specificatio

49 Cox proportional hazard models were used to identify risk factors for incidence of GA.

50 her's exact and non-parametric Wilcoxon rank-sum tests were used to identify significant differences in single-nucleotide