The steps of text mining in biomedical field and the methods used in its each step were described with stress laid on the tools used in each step of text mining in order to promote text mining in biomedical field.

The long-year program for medical education is the major way to train the high level medical professionals.This paper colleeted the research papers on this topic published in Chinese journals in recent years,extracted their key words and counted the high frequent key words According to the co-occurrence of a pair of key words in one paper.these high frequent keywords were clustered into groups.The research structure and main directions of the long-year program were outlined as the study of macro-planning of long-year program,the study of teaching models and teaching system,study of teaching practice.These results could be a useful reference for the medical educational researchers and managers.

Co-citation analysis, co-word analysis and strategic coordinates are combined to make known the subject theme evolu-tion. The research history of general surgery is described by eo-citation eluster analysis, citation strategic diagram is pictured by the theory of strategy coordinates to learn more about the novelty and attention of each hot topic. The present research focused on general surgery that would be found by co-words cluster analysis, and stages of development about each topic are discussed by strategic coordinates based of cluster results. It is helpful to provide a decision-making reference for professionals and managers.

After the basic properties of literature-related citation network, co-authorship network and co-words network were analyzed and the advances in their application research were summarized in aspects of their construc-tion methods, size and research depth, it was pointed out that article similarity networks could be constructed using the article similarity algorithm, and their basic properties and features were analyzed.

Co-citations of highly cited papers on gene annotation covered in Web of Science were analyzed by clustering analysis using clustering software after the word matrix of resource literature and highly cited papers was formed, which showed that the application of text mining on gene annotation includes use of authorized tools, development of text mining tools and algorithms, and verification of text mining tools.

Co-citations of foreign and domestic highly cited papers on drug target discovery were analyzed by clustering analysis using BICOMB2.01 and gCLUTO.Semantic analysis of the titles and abstracts in these highly cited papers and their important source literature showed that general trend, theoretical foundation, main methods and principal resources are the major hotspots of text mining in drug target discovery.

Text mining provides a new approach for drug repositioning, and the emerging databases and their corresponding tools provide more and more convenience for drug repositioning by text mining.The methods and tools for studying drug repositioning by text mining and their successful application examples were thus described in this paper in order to provide reference for the researchers interested in this field.

After the necessity and feasibility to detect drug adverse effects by text-mining were analyzed, the cur-rent researches on detection drug adverse effects by text-mining, unsolved problems and future development were summarized in aspects of text-mining process, text mining/detecting methods, results assessment, and current tool software.

A paper similarity network was constructed in light of semantic similarity algorithm using the complex network processing package , igraph in R language , and analyzed by random walk-trap algorithm , label propagation algorithm, BGII algorithm, and Girvan-Newman algorithm, respectively.The accuracy and stability of these 4 al-gorithms were compared according to the golden standards and the D function for network community classification evaluation index, which showed that the accuracy and stability of random walk-trap algorithm were better than those of the other 3 algorithms and preconditioning of complex network was an important influencing factor for clustering .

Co-occurrence network of major MeSH terms and all MeSH terms was constructed respectively with MEDLINE-covered pharmacological literature as its example.The degree, closeness and betweenness of centrality in different nodes of the two co-occurrence networks were analyzed , the distribution of scale-free properties in the two co-occurrence networks was tested, the small-world effect of the two co-occurrence networks was identified by comparing their overall properties ( average distance and clustering coefficient ) , which showed the scale-free properties and small-world effect of the two co-occurrence networks.The co-occurrence network of all MeSh terms could thus be used in analysis of network properties while that of major MeSH terms could thus be used in analysis of subject contents.