| In recent years,bioinformatics has gradually developed into one of the most concerned interdisciplinary directions.Within this discipline,a large number of research fields are developing rapidly.At the same time,a large number of scientific and technological literatures were generated,and the existing traditional methods could not satisfy the analysis of scientific research cooperation network in this field.In this paper,the LDA topic model and clustering algorithm are used to process the data before the scientific research cooperation network analysis,so as to help us obtain a series of meaningful analysis results.Topic model is a probabilistic generation model,which is widely used in many aspects,especially in natural language processing.Its appearance can help us quickly clear the hidden information in the massive text data.Nowadays,in the era when machine learning is widely used,thematic model has been recognized by the academic community.Compared with the semantic method based on ontology and other knowledge bases,the semantic content extracted from the topic model is richer and more suitable for cross-domain knowledge discovery.As another very important research field,complex network has been widely used in the research and analysis of network problems in various disciplines.Among them,the social network is a relatively mature research direction in the complex network research.In this paper,a co-author network is established based on the subject model and the data after clustering analysis,and further analysis is made.Coauthor network is a kind of common form of scientific cooperation network,which is established according to the cooperative relationship among the authors of the paper.If two authors co-sign one or more papers,there will be a link between the two authors.The network of co-authors can provide a powerful tool for the study of the structure of the network of academic relations.The study of soybean metabolism contributes to a better understanding of crop growth,yield and quality.Through in-depth analysis of the scientific and technological literature in the field of soybean metabolism,we can grasp the development trend of this field.Through tracking the relevant important figures and institutions in the field of soybean metabolism,we can grasp the latest trends in this field and grasp the frontier.First of all,the basic concepts of complex networks,bioinformatics and thematic models are reviewed.At the same time,based on the bioinformatics direction,soybean metabolism is taken as the key word,and the original data set is obtained according to the actual situation.Among them,all the data were from Web of Science,and the time was selected from 2000 to 2016.Then,we preprocessed the data,modeled the abstract literature separately,and conducted clustering processing,analyzed the results of the ten categories of clustering,and established the word cloud for each category,and intuitively had an overall cognition of each category through the word cloud.After processing the ten kinds of results and obtaining the network data,we study their statistical properties and analyze their significance.In addition,the data set is further analyzed and discussed based on the time dimension.The important node problem in the network is analyzed emphatically.We believe that the research in this paper can better improve our understanding of the network of scientific research cooperation,especially the network mechanism formed in the field of bioinformatics.Better understanding of current patterns and trends in key areas.This allows researchers to track major advances in current academic research. |
PDF Full Text Request