ArticleBMC bioinformatics2019
Identifying Alzheimer's disease-related proteins by LRRGD.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Hypoglycemia and Alzheimer Disease Risk: The Possible Role of Dasiglucagon.Cellular and molecular neurobiology · 2024Review
- Leveraging Computational Intelligence Techniques for Diagnosing Degenerative Nerve Diseases: A Comprehensive Review, Open Challenges, and Future Research Directions.Diagnostics (Basel, Switzerland) · 2023Review
- Article
- Trajectories and predictors of social avoidance in female patients with breast cancer.Frontiers in psychiatry · 2022Article
- MADGAN: unsupervised medical anomaly detection GAN using multiple adjacent brain MRI slice reconstruction.BMC bioinformatics · 2021Article
- Genetic Mechanism Revealed of Age-Related Macular Degeneration Based on Fusion of Statistics and Machine Learning Method.Frontiers in genetics · 2021Article
- Identifying diseases that cause psychological trauma and social avoidance by GCN-Xgboost.BMC bioinformatics · 2020Article
- Identifying Protein Biomarkers in Blood for Alzheimer's Disease.Frontiers in cell and developmental biology · 2020Article
- Identifying Cancer-Related lncRNAs Based on a Convolutional Neural Network.Frontiers in cell and developmental biology · 2020Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAlzheimer's disease (AD) imposes a heavy burden on society and every family. Therefore, diagnosing AD in advance and discovering new drug targets are crucial, while these could be achieved by identifying AD-related proteins. The time-consuming and money-costing biological experiment makes researchers turn to develop more advanced algorithms to identify AD-related proteins.
resultsFirstly, we proposed a hypothesis "similar diseases share similar related proteins". Therefore, five similarity calculation methods are introduced to find out others diseases which are similar to AD. Then, these diseases' related proteins could be obtained by public data set. Finally, these proteins are features of each disease and could be used to map their similarity to AD. We developed a novel method 'LRRGD' which combines Logistic Regression (LR) and Gradient Descent (GD) and borrows the idea of Random Forest (RF). LR is introduced to regress features to similarities. Borrowing the idea of RF, hundreds of LR models have been built by randomly selecting 40 features (proteins) each time. Here, GD is introduced to find out the optimal result. To avoid the drawback of local optimal solution, a good initial value is selected by some known AD-related proteins. Finally, 376 proteins are found to be related to AD.
conclusionThree hundred eight of three hundred seventy-six proteins are the novel proteins. Three case studies are done to prove our method's effectiveness. These 308 proteins could give researchers a basis to do biological experiments to help treatment and diagnostic AD.
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