Abstract
Alzheimer’s disease (AD) is a type of neurodegenerative disease that has become the fourth “health killer” for the elderly after cardiovascular, malignant, and stroke. Identifying AD-related proteins can effectively help diagnose diseases in advance, discover new drug targets, and gain a deeper understanding of the pathogenesis of disease. Due to the high cost of biological experiments, more and more researchers introduced advanced algorithms into this field. Based on the hypothesis of “similar diseases shares similar related proteins in this paper, diseases which are similar to AD were found by five similarity calculation methods. Then, related proteins of each disease were obtained by public data set. Through these proteins, features are extracted. Then, these features are mapped to disease similarity by Logistic Regression (LR). To avoid curse of dimensionality, random features are selected each time and hundreds of models were built. For each model, AD-related proteins could be obtained by Gradient Descent method. Finally, we integrated all the models and get all the proteins related to AD by weight counting. Finally, we did three case studies to prove novel proteins found by us are reliable.
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