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Research of Complex Detection Algorithms in Protein Interaction Network

Author: TangZuo
Tutor: YangZhiHao
School: Dalian University of Technology
Course: Applied Computer Technology
Keywords: Protein relationship Protein network of relationships Complex extraction Supervised learning
CLC: Q51-3
Type: Master's thesis
Year: 2011
Downloads: 12
Quote: 0
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Abstract


With the realization of the large number of biomedical experiments in the biomedical community, vast amounts of protein relational data, the these proteins relations constitute protein relational database. Mass relationship of protein to form a complex network, to extract effective information to become a research hotspot in the complex network of relationships, which the complex prediction module structure prediction is this important topic. The protein complex consists of two or more proteins, these proteins interact together to complete a biological function, has an important role in biological processes. Therefore, the discovery and study of the complex is of great significance to study the organization of biological cells and biological function. A network constituted by the large number of proteins relational data is exactly the data provides a good basis for automatically extracting complex. This paper first introduces the complex extraction technology and research profile, classic complex found a brief description of the algorithm, and analyze its problems. Then analyzed the structural features of biological networks and the complex nature of the network, due to the complex network module, so found the complex to become possible. For the problems of the classic method, a complex algorithm based on supervised learning method based on the structure of the multi-core fusion complex found discovery algorithm. Based on the structure of the multi-core fusion complex algorithm to solve the most complex discovery algorithm complex shortcomings identified only based on a single network. In this paper, three different networks, we take a different approach to extract the candidate core structure, then the candidate core structure of the fusion, and then filter structure of the candidate core collection and better by the determination of the attachment protein complex prediction results. Finally, the disadvantage of a supervised learning complex discovery algorithms to solve the traditional unsupervised method can not take advantage of a variety of complex information. This method will be a variety of information as a complex found characteristics, such as gene ontology information, rights reunited class coefficient, etc., are built from the feature set containing eighteen characterized. And introducing a model of the three categories, using the regression method of model training. Training model for complex discovery algorithm, the complex complete subgraph discovery algorithm. Complex found the experiment from the comparison of the parameters, the multiple angles of model comparison feature comparisons and other methods comparison experiments, the experimental results show that this method can effectively extract complex. In short, from the multi-network convergence complex and the use of supervised learning methods to improve the complex research discovery algorithm two angles. Experimental results show that the supervised learning is applied to a complex field is found feasible, and can better achieve the purpose of complex found.

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CLC: > Biological Sciences > Biochemistry > Protein
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