title: Protein interaction network constructing based on text mining and reinforcement learning with application to prostate cancer.
abstract: Constructing interaction network from biomedical texts is a very important and interesting work. The authors take advantage of text mining and reinforcement learning approaches to establish protein interaction network. Considering the high computational efficiency of co-occurrence-based interaction extraction approaches and high precision of linguistic patterns approaches, the authors propose an interaction extracting algorithm where they utilise frequently used linguistic patterns to extract the interactions from texts and then find out interactions from extended unprocessed texts under the basic idea of co-occurrence approach, meanwhile they discount the interaction extracted from extended texts. They put forward a reinforcement learning-based algorithm to establish a protein interaction network, where nodes represent proteins and edges denote interactions. During the evolutionary process, a node selects another node and the attained reward determines which predicted interaction should be reinforced. The topology of the network is updated by the agent until an optimal network is formed. They used texts downloaded from PubMed to construct a prostate cancer protein interaction network by the proposed methods. The results show that their method brought out pretty good matching rate. Network topology analysis results also demonstrate that the curves of node degree distribution, node degree probability and probability distribution of constructed network accord with those of the scale-free network well.
authors.affiliation: Collaborative Innovation Center of Novel Software Technology and Industrialization, People's Republic of China. zhufei@suda.edu.cn.
authors.forename: Fei
authors.initials: F
authors.lastname: Zhu
authors.affiliation: Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, People's Republic of China.
authors.forename: Quan
authors.initials: Q
authors.lastname: Liu
authors.affiliation: School of Computer Science and Technology, Soochow University, Suzhou 215006, People's Republic of China.
authors.forename: Xiaofang
authors.initials: X
authors.lastname: Zhang
authors.affiliation: Center for Systems Biology, Soochow University, Suzhou 215006, People's Republic of China.
authors.forename: Bairong
authors.initials: B
authors.lastname: Shen
mesh_terms: D057225:Data Mining / Q000379:methods*; D006801:Humans; D000069550:Machine Learning; D008297:Male; D009323:Natural Language Processing; D009363:Neoplasm Proteins / Q000378:metabolism*; D010506:Periodicals as Topic; D011471:Prostatic Neoplasms / Q000378:metabolism*; D025941:Protein Interaction Mapping / Q000379:methods*; D039781:PubMed; D015398:Signal Transduction
chemical_list: D009363:Neoplasm Proteins