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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Fei Zhu Quan Liu Xiaofang Zhang Bairong Shen |
| Copyright Year | 2014 |
| Description | Author affiliation: Sch. of Comput. Sci. & Technol., Soochow Univ., Suzhou, China (Fei Zhu; Quan Liu; Xiaofang Zhang) || Center for Syst. Biol., Soochow Univ., Suzhou, China (Bairong Shen) |
| Abstract | As a notoriously lethal human disease, cancer has obtained much concern for a long time. There have accumulated huge amounts of literature and experimental data on cancer-related research. It is impossible for people to deal with these texts manually to discover novel information and knowledge. However, text mining has an advantage of extracting previously unknown and understandable knowledge from large amounts of texts, and forming well-defined knowledge, providing the possibility to fully taking use of the existed texts. With the proceeding of biomedical research, people have gradually realized that complex biological functions and the phenomenon of life are the results of complex interactions among a variety of biological entities, such as protein. Deeply studying protein interaction network is essential to understand life. We, adopting reinforcement learning idea, put forward an algorithm for protein interaction network constructing. With the algorithm, nodes are used to represent proteins and edges denote interactions. During the evolutionary process, a node selects with which nodes in the network it tends to interact. Keep selecting and carrying on iteration, until eventually attaining an optimal network. The network is the result of the dynamic nature of learning behavior. As a malignancy, prostate cancer has been concerned for a long time. We attain biological texts from PubMed and establish a prostate cancer protein interaction networks by the proposed methods. The results show that our proposed method is pretty good. Network topology analysis results also show that the network node degree distribution is scale-free. |
| Starting Page | 46 |
| Ending Page | 51 |
| File Size | 352407 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781479956692 |
| DOI | 10.1109/BIBM.2014.6999302 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-11-02 |
| Publisher Place | UK |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Proteins Text mining systems biology Protein engineering prostate cancer protein interaction network Learning (artificial intelligence) text mining Prostate cancer reinforcement learning |
| Content Type | Text |
| Resource Type | Article |
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