AD-Syn-Net: systematic identification of Alzheimer’s disease-associated mutation and co-mutation vulnerabilities via deep learning
| Autor: | Xingxin Pan, Zeynep H Coban Akdemir, Ruixuan Gao, Xiaoqian Jiang, Gloria M Sheynkman, Erxi Wu, Jason H Huang, Nidhi Sahni, S Stephen Yi |
|---|---|
| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | Briefings in Bioinformatics. 24 |
| ISSN: | 1477-4054 1467-5463 |
| Popis: | Alzheimer’s disease (AD) is one of the most challenging neurodegenerative diseases because of its complicated and progressive mechanisms, and multiple risk factors. Increasing research evidence demonstrates that genetics may be a key factor responsible for the occurrence of the disease. Although previous reports identified quite a few AD-associated genes, they were mostly limited owing to patient sample size and selection bias. There is a lack of comprehensive research aimed to identify AD-associated risk mutations systematically. To address this challenge, we hereby construct a large-scale AD mutation and co-mutation framework (‘AD-Syn-Net’), and propose deep learning models named Deep-SMCI and Deep-CMCI configured with fully connected layers that are capable of predicting cognitive impairment of subjects effectively based on genetic mutation and co-mutation profiles. Next, we apply the customized frameworks to data sets to evaluate the importance scores of the mutations and identified mutation effectors and co-mutation combination vulnerabilities contributing to cognitive impairment. Furthermore, we evaluate the influence of mutation pairs on the network architecture to dissect the genetic organization of AD and identify novel co-mutations that could be responsible for dementia, laying a solid foundation for proposing future targeted therapy for AD precision medicine. Our deep learning model codes are available open access here: https://github.com/Pan-Bio/AD-mutation-effectors. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|