| Nome |
# |
|
The implication of different pruining methods on apple training systems, file e1dbfeaa-804e-4ac9-e053-1705fe0a1c61
|
717
|
|
MetaDB a data processing workflow in untargeted MS-based metabolomics experiments, file e1dbfeaa-7cab-4ac9-e053-1705fe0a1c61
|
398
|
|
The genetic bases of stilbenoids biosynthesis upon downy mildew infection in grapevine, file e1dbfeaa-855b-4ac9-e053-1705fe0a1c61
|
315
|
|
Meta-statistics for biomarker selection
in the omics sciences, file e1dbfeaa-835d-4ac9-e053-1705fe0a1c61
|
310
|
|
The WEIZMASS spectral library for high-confidence metabolite identification, file e1dbfeaa-e55f-4ac9-e053-1705fe0a1c61
|
255
|
|
Transcriptome analysis during berry development provides insights into co-regulated and altered gene expression between a seeded wine grape variety and its seedless somatic variant, file e1dbfeaa-c5a7-4ac9-e053-1705fe0a1c61
|
239
|
|
Meta-statistics for biomarker selection
in the omics sciences, file e1dbfeaa-6e05-4ac9-e053-1705fe0a1c61
|
232
|
|
A comparison of computational approaches for maximum likelihood estimation of the Dirichlet parameters on high dimensional
data, file e1dbfeaa-7b0d-4ac9-e053-1705fe0a1c61
|
224
|
|
Self-organizing maps: a versatile tool for the automatic analysis of untargeted imaging datasets, file e1dbfeaa-815c-4ac9-e053-1705fe0a1c61
|
219
|
|
Meta-statistics for variable selection: the R package BioMark, file e1dbfeaa-689c-4ac9-e053-1705fe0a1c61
|
194
|
|
Oxygen and hydrogen stable isotope ratios
of bulk needles reveal the geographic origin
of Norway spruce in the European Alps, file e1dbfeaa-841f-4ac9-e053-1705fe0a1c61
|
190
|
|
Biomarker selection for omics data, file e1dbfeaa-7af6-4ac9-e053-1705fe0a1c61
|
177
|
|
Stable isotope ratios of H, C, N and O in Italian citrus juices, file e1dbfeaa-83e1-4ac9-e053-1705fe0a1c61
|
175
|
|
1H NMR-based metabolomics: Chemometric methods for the diagnosis of inborn errors of metabolism, file e1dbfeaa-62cf-4ac9-e053-1705fe0a1c61
|
159
|
|
Transcriptome analysis during berry development provides insights into co-regulated and altered gene expression between a seeded wine grape variety and its seedless somatic variant, file e1dbfeaa-7cb0-4ac9-e053-1705fe0a1c61
|
150
|
|
Parametric time warping in metabolomics, file e1dbfeaa-827d-4ac9-e053-1705fe0a1c61
|
128
|
|
Rpv3 locus and stilbenoid induction mediate downy mildew resistance in the Merzling x Teroldego segregating population, file e1dbfeaa-f97f-4ac9-e053-1705fe0a1c61
|
125
|
|
MS imaging of small metabolites in fruits, file e1dbfeaa-6205-4ac9-e053-1705fe0a1c61
|
104
|
|
Statistical methods for improving authentication of wines based on stable isotope ratios, file e1dbfeaa-75e3-4ac9-e053-1705fe0a1c61
|
93
|
|
Characterization of downy mildew resistance in a grapevine segregating population by integration of metabolite and disease symptoms analysis, file e1dbfeaa-7bea-4ac9-e053-1705fe0a1c61
|
88
|
|
Interpreting isotope data of the Italian wine databank for regional discrimination: comparison between univariate and multivariate statistical methods, file e1dbfeaa-684f-4ac9-e053-1705fe0a1c61
|
80
|
|
Establishing an GC-MS based metabolomics platform, file e1dbfeaa-6a69-4ac9-e053-1705fe0a1c61
|
78
|
|
Establishing an GC-MS based metabolomics platform, file e1dbfeaa-6a68-4ac9-e053-1705fe0a1c61
|
71
|
|
Grape isoprenoids: automated data analysis in more than 500 samples, file e1dbfeaa-7543-4ac9-e053-1705fe0a1c61
|
70
|
|
Gibberellins metabolism in Vitis vinifera at flowering and fruit set, file e1dbfeaa-6833-4ac9-e053-1705fe0a1c61
|
69
|
|
Stability selection for metabolomics data, file e1dbfeaa-77f3-4ac9-e053-1705fe0a1c61
|
64
|
|
Biomarker selection in R: the BioMark package, file e1dbfeaa-6895-4ac9-e053-1705fe0a1c61
|
55
|
|
Comparison of computational approaches for maximum likelihood estimation on compositional data, file e1dbfeaa-836b-4ac9-e053-1705fe0a1c61
|
48
|
|
Traceability along the production chain of Italian tomato products, file e1dbfeaa-5dae-4ac9-e053-1705fe0a1c61
|
47
|
|
Bioinformatic solutions for addressing the grape metabolome, file e1dbfeaa-72e0-4ac9-e053-1705fe0a1c61
|
44
|
|
Targeted metabolomics employed for the studying of ‘Pinot noir’ grape skin phenolics as induced by canopy microclimate manipulation, file e1dbfeaa-764d-4ac9-e053-1705fe0a1c61
|
44
|
|
New tools for the analysis of Mass Spectrometry based metabolic images of plant tissues, file e1dbfeaa-6894-4ac9-e053-1705fe0a1c61
|
42
|
|
Innovative bionformatic tools for the analysis of MS based imaging dataset in plant metabolomics, file e1dbfeaa-66f2-4ac9-e053-1705fe0a1c61
|
41
|
|
Analysis of untargeted MS-based metabolomics data: the metaMS package for R, file e1dbfeaa-815b-4ac9-e053-1705fe0a1c61
|
41
|
|
Stability selection for omics data, file e1dbfeaa-683a-4ac9-e053-1705fe0a1c61
|
39
|
|
Stability selection for metabolomics data, file e1dbfeaa-7ada-4ac9-e053-1705fe0a1c61
|
38
|
|
Statistical modeling of metabolic profiling data based on sensory attributes, file e1dbfeaa-836d-4ac9-e053-1705fe0a1c61
|
36
|
|
Assessing cutoff points for biomarker selection in -omics technologies, file e1dbfeaa-5d94-4ac9-e053-1705fe0a1c61
|
35
|
|
The genetic bases of downy mildew resistance and stilbenoids production in a grapevine inter-specific crossing population., file e1dbfeaa-de48-4ac9-e053-1705fe0a1c61
|
35
|
|
Genetic investigation of seed formation during berry development using RNA-Seq reveals transcriptional changes in grapevine (Vitis vinifera L.), file e1dbfeaa-671b-4ac9-e053-1705fe0a1c61
|
33
|
|
Transcriptome analysis during berry development provides insights into co-regulated and altered gene expression between a seeded wine grape variety and its seedless somatic variant, file e1dbfeaa-7cb1-4ac9-e053-1705fe0a1c61
|
33
|
|
Self-organizing maps: a versatile tool for the analysis of untargeted imaging datasets, file e1dbfeaa-7d00-4ac9-e053-1705fe0a1c61
|
33
|
|
Highthroughput metabolomics challenges for bioinformatics, file e1dbfeaa-65f3-4ac9-e053-1705fe0a1c61
|
32
|
|
The rare case of a cluster mutation in grapevine: a metabolomics study, file e1dbfeaa-6a6b-4ac9-e053-1705fe0a1c61
|
32
|
|
A fully automated pipeline for the analysis of Liquid Chromatography-Mass Spectrometry (LC-MS) based metabolomics experiments., file e1dbfeaa-6e06-4ac9-e053-1705fe0a1c61
|
31
|
|
The rare case of a cluster mutation in grapevine: a metabolomics study, file e1dbfeaa-6a6c-4ac9-e053-1705fe0a1c61
|
19
|
|
Linking GC-MS and PTR-TOF-MS fingerprints of food samples, file e1dbfeaa-681f-4ac9-e053-1705fe0a1c61
|
4
|
|
Studio delle basi genetiche della resistenza a peronospora e della produzione di polifenoli in una popolazione di vite ottenuta da incrocio interspecifico., file e1dbfeaa-f180-4ac9-e053-1705fe0a1c61
|
4
|
|
The use of IRMS, 1H NMR and chemical analysis to characterise Italian and imported Tunisian olive oils, file e1dbfeaa-e64e-4ac9-e053-1705fe0a1c61
|
3
|
|
The influence of training system on apple fruit quality, file e1dbfeaa-8cec-4ac9-e053-1705fe0a1c61
|
2
|
|
High-throughput carotenoid profiling using multivariate curve resolution, file e1dbfeaa-6c46-4ac9-e053-1705fe0a1c61
|
1
|
|
metaMS: an open-source pipeline for GC-MS-based untargeted metabolomics, file e1dbfeaa-7fb3-4ac9-e053-1705fe0a1c61
|
1
|
|
Climatic and geographical dependence of the H, C and O stable isotope ratios of Italian wine, file e1dbfeaa-aa09-4ac9-e053-1705fe0a1c61
|
1
|
|
On the maximization of likelihoods belonging to the exponential family using a Levenberg–Marquardt approach, file e1dbfeab-072c-4ac9-e053-1705fe0a1c61
|
1
|
| Totale |
5.699 |