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Identifying Alzheimer's disease-associated genes using PhenoGeneRanker

Rahman, M. T.; Saeed, F.; Bozdag, S.

2024-11-15 bioinformatics
10.1101/2024.11.12.623269 bioRxiv
Show abstract

Alzheimers disease (AD) is a neurogenerative disease that affects millions worldwide with no effective treatment. Several studies have been conducted to decipher to genomic underpinnings of AD. Due to its complex nature, many genes have been found to be associated with AD. Despite these findings, the pathophysiology of the disease is still elusive. To discover new putative AD-associated genes, in this study, we integrated multimodal gene and phenotype datasets of AD using network biology methods to prioritize potential AD-related genes. We constructed a multiplex heterogeneous network composed of patient and gene similarity networks utilizing phenotypic and omics datasets of AD patients from the Alzheimers Disease Neuroimaging Initiative (ADNI) database. We applied PhenoGeneRanker to traverse this network to discover potential AD-associated genes. To assess the impact of each network layer and seed gene, we also run PhenoGeneRanker on different variants of the network and seed genes. Our results showed that top-ranked genes captured several known AD-related genes and were enriched in Gene Ontology (GO) terms related to AD. We also observed that several top-ranked genes that are not in AD-associated gene list had literature supporting their potential relevance to AD.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

1
Journal of Alzheimer’s Disease
○ 50 papers in training set
Top 0.1%
23.2%
Similar papers in this journal
2
Scientific Reports
○ 3612 papers in training set
Top 3%
11.4%
Similar papers in this journal
3
Computational and Structural Biotechnology Journal
● 242 papers in training set
Top 0.3%
7.0%
Similar papers in this journal
4
Alzheimer's Research & Therapy
○ 57 papers in training set
Top 0.3%
5.0%
Similar papers in this journal
5
Alzheimer's & Dementia
177 papers in training set
Top 1%
4.2%
50% of probability mass above
6
Frontiers in Genetics
○ 230 papers in training set
Top 1%
2.9%
7
PLOS ONE
● 5266 papers in training set
Top 38%
2.7%
8
Briefings in Bioinformatics
◐ 354 papers in training set
Top 3%
2.7%
9
Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring
○ 42 papers in training set
Top 0.5%
2.5%
10
Bioinformatics
◐ 1204 papers in training set
Top 6%
2.5%
11
npj Systems Biology and Applications
○ 125 papers in training set
Top 0.7%
2.2%
12
BMC Bioinformatics
○ 457 papers in training set
Top 5%
1.2%
13
Nature Communications
○ 5641 papers in training set
Top 49%
1.2%
14
Communications Biology
○ 993 papers in training set
Top 20%
1.2%
15
Computers in Biology and Medicine
○ 128 papers in training set
Top 3%
1.2%
16
iScience
○ 1154 papers in training set
Top 24%
1.2%
17
GeroScience
○ 109 papers in training set
Top 1%
1.1%
18
Informatics in Medicine Unlocked
○ 22 papers in training set
Top 1.0%
1.0%
19
Journal of Alzheimer's Disease
○ 48 papers in training set
Top 0.9%
1.0%
20
Molecular Psychiatry
○ 282 papers in training set
Top 5%
0.9%
21
Alzheimer's & Dementia: Translational Research & Clinical Interventions
○ 17 papers in training set
Top 0.5%
0.9%
22
npj Digital Medicine
○ 118 papers in training set
Top 3%
0.9%
23
Acta Neuropathologica Communications
○ 89 papers in training set
Top 2%
0.9%
24
eLife
● 5828 papers in training set
Top 65%
0.9%
25
Molecular Neurodegeneration
○ 55 papers in training set
Top 2%
0.6%
26
Frontiers in Human Neuroscience
○ 77 papers in training set
Top 2%
0.6%
27
Genomics
○ 64 papers in training set
Top 2%
0.6%
28
npj Aging
○ 22 papers in training set
Top 0.5%
0.6%
29
Neurobiology of Aging
○ 107 papers in training set
Top 2%
0.5%
30
Journal of the Neurological Sciences
○ 18 papers in training set
Top 0.7%
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.