Back

Precision-targeted metabolomics method characterizes differential metabolomes in multiple-biological matrixes and cell-mitochondria

Luo, X.; Guo, R.; Liu, J.; Zhang, A.; Wang, X.; Lu, H.

2020-03-09 biochemistry
10.1101/642496 bioRxiv
Show abstract

This new method has the capacity to dynamically analyse the metabolome of interest in diverse biological matrixes by offering coverage of rat urine, plasma, liver, brain, intestine, stomach, heart, spleen, lung, faeces, fresh plant tissues, cells and microbes. In addition, this new method enables specific and efficient analysis of microdontia metabolomes, non-microdontia and whole cell metabolomes, as well as can engage in absolute determination of 84 key clinical-wide metabolites in different biological matrixes, to enable the complementary support of clinical diagnosis and classification of diseases. To demonstrate the applicable capacity of this new method, multiple-matrixes differential metabolomes were firstly characterized using this new method to coordinate metabolic modifications underlie hepatitis induced by carbon tetrachloride (CCL4) in rats, such finding provides novel insight into the pathogenesis and therapeutics of hepatitis in clinic. Altogether, we are fully confident that this new metabolomics method will be widely welcomed by scientists in different niches to solve their key questions accordingly.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

1
Analytical Chemistry
● 218 papers in training set
Top 0.1%
23.6%
Similar papers in this journal
2
Analytica Chimica Acta
○ 17 papers in training set
Top 0.1%
8.3%
Similar papers in this journal
3
Metabolites
○ 53 papers in training set
Top 0.1%
8.3%
Similar papers in this journal
4
Scientific Reports
○ 3612 papers in training set
Top 23%
4.3%
Similar papers in this journal
5
PLOS ONE
● 5266 papers in training set
Top 31%
4.3%
Similar papers in this journal
6
Journal of Proteome Research
● 234 papers in training set
Top 0.7%
3.6%
50% of probability mass above
7
eLife
● 5828 papers in training set
Top 33%
3.4%
8
Journal of the American Society for Mass Spectrometry
● 37 papers in training set
Top 0.2%
2.2%
9
Nature Communications
○ 5641 papers in training set
Top 42%
2.1%
10
Talanta
○ 14 papers in training set
Top 0.1%
2.0%
11
The Analyst
● 16 papers in training set
Top 0.1%
2.0%
12
Metabolomics
○ 14 papers in training set
Top 0.1%
2.0%
13
Analytical Biochemistry
○ 26 papers in training set
Top 0.2%
1.8%
14
Journal of Agricultural and Food Chemistry
● 15 papers in training set
Top 0.4%
1.2%
15
International Journal of Molecular Sciences
○ 494 papers in training set
Top 10%
1.2%
16
Food & Function
● 13 papers in training set
Top 0.2%
1.2%
17
Communications Chemistry
○ 48 papers in training set
Top 0.8%
1.1%
18
Scientific Data
○ 209 papers in training set
Top 2%
1.0%
19
DNA Research
◐ 26 papers in training set
Top 0.3%
0.9%
20
Frontiers in Chemistry
○ 16 papers in training set
Top 0.3%
0.9%
21
Biomolecules
○ 100 papers in training set
Top 2%
0.9%
22
Communications Biology
○ 993 papers in training set
Top 28%
0.9%
23
Food Chemistry
○ 13 papers in training set
Top 0.2%
0.9%
24
Journal of Proteomics
○ 28 papers in training set
Top 0.4%
0.9%
25
Analytical and Bioanalytical Chemistry
○ 18 papers in training set
Top 0.3%
0.9%
26
Clinical Proteomics
○ 11 papers in training set
Top 0.2%
0.6%
27
Molecular Omics
◐ 23 papers in training set
Top 0.4%
0.6%
28
Water Research
○ 79 papers in training set
Top 1%
0.6%
29
Pharmacological Research
○ 18 papers in training set
Top 0.8%
0.5%
30
ACS Omega
● 105 papers in training set
Top 4%
0.5%

"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.