Research papers related bioinformatics
In conclusion, most previous studies identified knowledge structures by adopting not only bibliometric analysis but text-mining techniques such as the LDA model. While each document consists of a set of topics in pLSI, using the LDA model a more precise manipulation is added to organize the topics.
Previous studies mainly rely on quantitative measures and suffer from the lack of content analysis. They divide the data into three time periods to analyze the changes of fields over time.
Bioinformatics impact factor 2018
We then describe the proposed method in the Methods section. Seglen and Aksnes [ 9 ] used the size and the productivity of research groups in the microbiology field in Norway as a measurement for bibliometric analysis. We analyze and discuss the results of leading topics, authors, and journals in the Result and Discussion section. The results of ACT model—based analysis show that various topics begin to appear and mixed subject topics become more apparent over time. Abstract Background Bioinformatics is an interdisciplinary field at the intersection of molecular biology and computing technology. Bioinformatics is one example. In addition, other factors such as the affiliation of authors, collaborations, and citation data are often incorporated into bibliometric analysis [ 6 — 9 ]. Article formats Research articles, Review papers, Workshop contributions if peer-reviewed Article processing charges All articles published by the Journal of Integrative Bioinformatics are fully open access.
Article formats Research articles, Review papers, Workshop contributions if peer-reviewed Article processing charges All articles published by the Journal of Integrative Bioinformatics are fully open access. Additionally, for further integrated analysis, we conduct a time series analysis among the top-ranked keyphrases, journals, and authors according to their frequency.
The research questions that we are to investigate in this paper are: 1 What are the topical trends of bioinformatics over time? However, the main limitation of this application of the LDA model,the representative method for trend analysis, is that it only explains topical trends by using one parameter such as bag-of-words on documents via topical terms.
Research papers related bioinformatics
Song and Kim [ 11 ] collected full-text articles from PubMed Central and computed their citation relation. The results of ACT model—based analysis show that various topics begin to appear and mixed subject topics become more apparent over time. These studies identify the knowledge structure of a certain field by constructing bibliometric networks or databases with text-mining techniques. Currently, there are about 1, database systems and various analytical tools available via the Internet which are directed at solving various biological tasks. The most prevalent approach is to apply topic modeling to content analysis as a part of bibliometrics. However, as opposed to revealing the topic structure in relation to metadata such as authors, publication date, and journals, LDA only displays the simple topic structure. To obtain more meaningful results, we use journals and keyphrases instead of conferences and bag-of-words.. Topic-modeling techniques are mostly adopted to identify the topics of a subject area while analyzing that area more abundantly [ 10 — 13 ]. We divided the collected datasets into four periods to trace the changes of topic, author, journal ranking over time, and combine the results with bibliometric analysis. These tools will also represent the backbone of the concept of a virtual cell. Kim et al. In addition, some studies use author information or the collaboration pattern among authors to understand the certain field.
To address this problem, we introduce a new algorithm specifically designed to quantify the likelihood of rare behaviours in SDE models. These results are confirmed by integrated analysis of topic distribution as well as top ranked keyphrases, authors, and journals.
Bornmann and Mutz [ 6 ] recently identified the development of modern science by bibliometric analysis. The primary purpose of these models is to study rare but interesting or important behaviours, such as the It is not sufficient to conduct comprehensive analysis for understanding knowledge disciplines.
On the one hand, analysis of this data uses essentially the methods and concepts of computer science; on the other hand, the range of biological tasks solved by researchers determines the range and scope of the data.
They infer the knowledge structure and understand the trend of the bioinformatics field.
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