Identifying Alzheimer’s disease-related proteins by LRRGD

• Published in: BMC bioinformatics Vol. 20; no. S18; pp. 570 - 9
• Main Authors: Zhao, Tianyi, Hu, Yang, Zang, Tianyi, Cheng, Liang
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 25.11.2019; BioMed Central; BMC
• Subjects: Advertising executives; Algorithms; Alzheimer Disease - diagnosis; Alzheimer Disease - genetics; Alzheimer Disease - metabolism; Alzheimer's disease; Archives & records; Artificial intelligence; Biochemistry; Biomarkers; Brain diseases; Case studies; Computational Biology - methods; Databases, Protein; Diagnostic systems; Family; Genes; Gradient descent; Humans; Logistic regression; Machine learning; Neurodegenerative diseases; Novels; Ontology; Parkinson's disease; Plasma; Proteins; Proteins - genetics; Proteins - metabolism; Similarity; Similarity of diseases; Proteins; Gradient descent; Logistic regression; Similarity of diseases; Alzheimer’s disease
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-019-3124-7

Alzheimer'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. Firstly, 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. Three 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.