Back

OmicsOne: Associate Omics Data with Phenotypes in One-Click

Hu, Y.; Ao, M.; Zhang, H.

2019-09-05 bioinformatics
10.1101/756544 bioRxiv
Show abstract

The rapid advancements of high-throughput \"omics\" technologies have brought huge amount of data to process during and after experiments. Multi-omic analysis facilitates a deeper interrogation of a dataset, and discovery of interesting genes, proteins, lipids, glycans, or metabolites, or pathways related to the corresponding phenotypes in a study. Many individual software tools have been developed to analyze and visualize the data. However, integrating multiple omics data analysis strategies and approaches in a single data processing pipeline is still a challenge task. OmicsOne is a software developed in R, Python and Jupyter Notebook that can achieve statistical analysis, machine learning, and data visualization on multi- omics data by taking the advantages of integrating the useful tools from individual software packages. OmicsOne can simplify \"omics\" data analysis, and delineate molecules, or pathways associated to interested phenotypes.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

1
Bioinformatics
◐ 1204 papers in training set
Top 0.7%
27.2%
Similar papers in this journal
2
BMC Bioinformatics
○ 457 papers in training set
Top 0.3%
13.0%
Similar papers in this journal
3
PROTEOMICS
○ 43 papers in training set
Top 0.1%
5.7%
Similar papers in this journal
4
Scientific Reports
○ 3612 papers in training set
Top 14%
5.6%
Similar papers in this journal
50% of probability mass above
5
PLOS ONE
● 5266 papers in training set
Top 30%
4.4%
Similar papers in this journal
6
Computational and Structural Biotechnology Journal
● 242 papers in training set
Top 1%
3.3%
7
Journal of Proteome Research
● 234 papers in training set
Top 0.8%
3.3%
8
Briefings in Bioinformatics
◐ 354 papers in training set
Top 3%
2.9%
9
Bioinformatics Advances
◐ 203 papers in training set
Top 2%
2.5%
10
Analytical Chemistry
● 218 papers in training set
Top 2%
1.5%
11
NAR Genomics and Bioinformatics
◐ 242 papers in training set
Top 3%
1.5%
12
Frontiers in Bioinformatics
○ 49 papers in training set
Top 0.6%
1.5%
13
International Journal of Molecular Sciences
○ 494 papers in training set
Top 10%
1.2%
14
BMC Medical Genomics
○ 50 papers in training set
Top 0.7%
1.2%
15
GigaScience
◐ 212 papers in training set
Top 3%
1.2%
16
F1000Research
○ 88 papers in training set
Top 3%
1.1%
17
PLOS Computational Biology
● 1863 papers in training set
Top 18%
1.1%
18
Frontiers in Genetics
○ 230 papers in training set
Top 5%
0.9%
19
SoftwareX
○ 15 papers in training set
Top 0.2%
0.9%
20
Molecular & Cellular Proteomics
183 papers in training set
Top 2%
0.7%
21
Journal of the American Society for Mass Spectrometry
● 37 papers in training set
Top 0.5%
0.6%
22
BioData Mining
○ 22 papers in training set
Top 0.8%
0.6%
23
Journal of Bioinformatics and Systems Biology
○ 15 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.