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Multi-omics Quality Assessment in Personalized Medicine through EATRIS

EATRIS Plus Multi-omics working group and stakeholders, ; Scherer, A.

2023-11-08 molecular biology
10.1101/2023.10.25.563912 bioRxiv
Show abstract

Molecular characterization of a biological sample, e.g., with omics approaches, is fundamental for the development and implementation of personalized and precision medicine approaches. In this context, quality assessment is one of the most critical aspects. Accurate performance and interpretation of omics techniques is based on consensus, harmonization, and standardization of protocols, procedures, data analysis and reference values and materials. EATRIS, the European Infrastructure for Translational Medicine (www.EATRIS.eu), brings together resources and services to support researchers in developing their biomedical discoveries into novel translational tools and interventions for better health outcomes. Here we describe activities of member facilities of EATRIS towards quality assessment of pre-clinical sample processing, clinical omics data generation, multi-omics data integration, and dissemination of the resources in a Multi-Omics Toolbox, the principal deliverable of the EATRIS Plus project for the consolidation of EATRIS towards translational Medicine.

Matching journals

The top 10 journals account for 50% of the predicted probability mass.

1
International Journal of Molecular Sciences
○ 494 papers in training set
Top 0.2%
10.2%
Similar papers in this journal
2
PLOS ONE
● 5266 papers in training set
Top 17%
10.0%
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3
Nucleic Acids Research
◐ 1281 papers in training set
Top 2%
8.2%
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4
Journal of Proteome Research
● 234 papers in training set
Top 0.5%
7.0%
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5
PROTEOMICS
○ 43 papers in training set
Top 0.2%
3.4%
Similar papers in this journal
6
Computational and Structural Biotechnology Journal
● 242 papers in training set
Top 2%
2.9%
7
Biology Methods and Protocols
◐ 61 papers in training set
Top 0.3%
2.9%
8
Clinical Chemistry
◐ 22 papers in training set
Top 0.1%
2.5%
9
Scientific Reports
○ 3612 papers in training set
Top 39%
2.5%
10
Frontiers in Public Health
○ 148 papers in training set
Top 3%
2.2%
50% of probability mass above
11
Cells
○ 249 papers in training set
Top 2%
2.0%
12
Analytical Chemistry
● 218 papers in training set
Top 1%
2.0%
13
Diagnostics
○ 50 papers in training set
Top 1%
1.8%
14
BMC Methods
○ 15 papers in training set
Top 0.1%
1.8%
15
Nature Communications
○ 5641 papers in training set
Top 44%
1.8%
16
Microbiology Spectrum
● 469 papers in training set
Top 7%
1.7%
17
BMC Genomics
○ 406 papers in training set
Top 4%
1.7%
18
Molecular & Cellular Proteomics
183 papers in training set
Top 1%
1.3%
19
Journal of Translational Medicine
○ 57 papers in training set
Top 1%
1.2%
20
Molecular Omics
◐ 23 papers in training set
Top 0.2%
1.2%
21
GigaScience
◐ 212 papers in training set
Top 3%
1.2%
22
Talanta
○ 14 papers in training set
Top 0.2%
1.2%
23
Communications Medicine
○ 113 papers in training set
Top 2%
1.2%
24
Bioinformatics
◐ 1204 papers in training set
Top 7%
1.2%
25
Briefings in Bioinformatics
◐ 354 papers in training set
Top 5%
1.2%
26
Data in Brief
○ 14 papers in training set
Top 0.1%
1.0%
27
Metabolites
○ 53 papers in training set
Top 0.9%
0.9%
28
Frontiers in Bioinformatics
○ 49 papers in training set
Top 2%
0.6%
29
BMC Bioinformatics
○ 457 papers in training set
Top 6%
0.6%
30
Laboratory Investigation
○ 13 papers in training set
Top 0.3%
0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.