XJIPC OpenIR  > 多语种信息技术研究室
Thesis Advisor程力
Degree Grantor中国科学院大学
Place of Conferral北京
Degree Name硕士
Degree Discipline计算机应用技术
Keyword蛋白质相互作用 深度学习 长短时记忆模型 位置特异性打分矩阵


Other Abstract

The physiological functions of living organisms are largely regulated by proteins in cells. The process of cell metabolism is mainly accomplished by protein complexes or binding of proteins to ligands. It is shown that protein-protein interaction not only dominates signal transduction and recognition, metabolism, but also affects the formation of complex protein complexes, cell cycle regulation, cancer occurrence and development. Therefore, the study of protein-protein interaction is a very important topic, which is not only conducive to a comprehensive and in-depth understanding of human life processes, but also conducive to exploring the mechanism of disease, the development of biopharmaceuticals and the precise search for drug targets.In recent years, the rapid development of high-throughput technology has greatly promoted the explosive growth of protein sequence information. how to make good use of the rich protein sequence information and determine whether protein-protein interaction will occur efficiently and accurately is a difficult problem to be solved. Because of the large amount of data and the complexity of interactive protein networks, the traditional experimental methods are not suitable for today's tasks. At this point, a more efficient, flexible, and cost-effective approach to machine learning offers us another way to address this unprecedented challenge. Based on the first-order sequence information of proteins, a protein interaction model based on depth learning is developed. the model takes the deep long-term memory neural network as the prediction model and considers the evolution information contained in the protein sequence information.The model has been successfully demonstrated on two protein interaction test sets. To demonstrate the ability of in-depth learning, we compare the results of our method with the well-known support vector machine method and several other known methods on the same data set. The results show that our method is far superior to other existing techniques in every index.

Document Type学位论文
Recommended Citation
GB/T 7714
王延斌. 基于深度学习的蛋白质相互作用预测研究[D]. 北京. 中国科学院大学,2018.
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