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mosGraphFlow: a novel integrative graph AI model mining disease targets from multi-omic data

Zhang, H.; Cao, D.; Xu, T.; Chen, E.; Li, G.; Chen, Y.; Payne, P.; Province, M.; Li, F.

2024-08-06 bioinformatics
10.1101/2024.08.01.606219 bioRxiv
Show abstract

Multi-omic data can better characterize complex cellular signaling pathways from multiple views compared to individual omic data. However, integrative multi-omic data analysis to rank key disease biomarkers and infer core signaling pathways remains an open problem. In this study, our novel contributions are that we developed a novel graph AI model, mosGraphFlow, for analyzing multi-omic signaling graphs (mosGraphs), 2) analyzed multi-omic mosGraph datasets of AD, and 3) identified, visualized and evaluated a set of AD associated signaling biomarkers and network. The comparison results show that the proposed model not only achieves the best classification accuracy but also identifies important AD disease biomarkers and signaling interactions. Moreover, the signaling sources are highlighted at specific omic levels to facilitate the understanding of the pathogenesis of AD. The proposed model can also be applied and expanded for other studies using multi-omic data. Model code is accessible via GitHub: https://github.com/FuhaiLiAiLab/mosGraphFlow

Matching journals

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

1
Journal of Alzheimer’s Disease
○ 50 papers in training set
Top 0.1%
19.0%
Similar papers in this journal
2
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● 242 papers in training set
Top 0.2%
8.1%
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3
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○ 3612 papers in training set
Top 8%
7.5%
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4
Alzheimer's Research & Therapy
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Top 0.2%
6.4%
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5
Alzheimer's & Dementia
177 papers in training set
Top 0.8%
6.2%
6
npj Systems Biology and Applications
○ 125 papers in training set
Top 0.4%
3.3%
50% of probability mass above
7
Computers in Biology and Medicine
○ 128 papers in training set
Top 1%
3.2%
8
Frontiers in Genetics
○ 230 papers in training set
Top 1%
2.9%
9
Briefings in Bioinformatics
◐ 354 papers in training set
Top 3%
2.5%
10
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○ 22 papers in training set
Top 0.4%
2.5%
11
PLOS ONE
● 5266 papers in training set
Top 40%
2.5%
12
iScience
○ 1154 papers in training set
Top 13%
2.2%
13
International Journal of Molecular Sciences
○ 494 papers in training set
Top 6%
2.2%
14
Neurobiology of Disease
○ 148 papers in training set
Top 2%
2.0%
15
Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring
○ 42 papers in training set
Top 0.7%
1.5%
16
Communications Biology
○ 993 papers in training set
Top 20%
1.2%
17
GeroScience
○ 109 papers in training set
Top 1%
1.1%
18
npj Aging
○ 22 papers in training set
Top 0.3%
1.1%
19
Molecular Psychiatry
○ 282 papers in training set
Top 5%
0.9%
20
Brain Communications
◐ 166 papers in training set
Top 3%
0.9%
21
eLife
● 5828 papers in training set
Top 65%
0.9%
22
Journal of Translational Medicine
○ 57 papers in training set
Top 2%
0.9%
23
Nature Communications
○ 5641 papers in training set
Top 55%
0.9%
24
Journal of Proteome Research
● 234 papers in training set
Top 2%
0.9%
25
Frontiers in Human Neuroscience
○ 77 papers in training set
Top 2%
0.9%
26
npj Digital Medicine
○ 118 papers in training set
Top 3%
0.9%
27
Genomics
○ 64 papers in training set
Top 2%
0.6%
28
Molecular Neurodegeneration
○ 55 papers in training set
Top 2%
0.6%
29
Journal of the Neurological Sciences
○ 18 papers in training set
Top 0.6%
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.