Abstract
Finding the most suitable co-author is one of the most important ways to conduct effective research in scientific fields. Data science has contributed to achieving this possibility significantly. The present study aims at designing a mathematical model of co-author recommender system in bioinformatics using graph mining techniques and big data applications. The present study employed an applied-developmental research method and a mixed-methods research design. The research population consisted of all scientific products in bioinformatics in the PubMed database. To achieve the research objectives, the most appropriate effective features in choosing a co-author were selected, prioritized, and weighted by experts. Then, they were weighted using graph mining techniques and big data applications. Finally, the mathematical co-author recommender system model in bioinformatics was presented. Data analysis instruments included Expert Choice, Excel, Spark, Scala and Python programming languages in a big data server. The research was conducted in four steps: (1) identifying and prioritizing the criteria effective in choosing a co-author using AHP; (2) determining the correlation degree of articles based on the criteria obtained from step 1 using algorithms and big data applications; (3) developing a mathematical co-author recommender system model; and (4) evaluating the developed mathematical model. Findings showed that the journal titles and citations criteria have the highest weight while the abstract has the lowest weight in the mathematical co-author recommender system model. The accuracy of the proposed model was 72.26. It was concluded that using content-based features and expert opinions have high potentials in recommending the most appropriate co-authors. It is expected that the proposed co-author recommender system model can provide appropriate recommendations for choosing co-authors on various fields in different contexts of scientific information. The most important innovation of this model is the use of a combination of expert opinions and systemic weights, which can accelerate the finding of co-authors and consequently saving time and achieving a greater quality of scientific products.
Highlights
Scientific collaboration in various fields has increased because of the growth in knowledge production and the increase in interdisciplinary knowledge
One of these network types is the static social networks type of which the bibliographic information networks subtype is significant. An example of such a network is the PubMed1 database. This information network consists of bibliographic data on medical science and information provided by the National Center for Biotechnology Information (NCBI) at the National Library of Medicine (NLM)
It is notable that we developed a mathematical model of the co-author recommender system using graph mining techniques or graph theory and big data applications
Summary
Scientific collaboration in various fields has increased because of the growth in knowledge production and the increase in interdisciplinary knowledge. An increase in scientific collaboration has been a prominent feature of the evolution of science, at least since the beginning of the twentieth century [2,3,4,5]. One of the researchers’ concerns in choosing a co-author is to find individuals who can help them achieve the best and most appropriate scientific results Identifying such individuals is researchers’ one of the most critical issues that can lead to saving time, achieving more efficiency, and synergizing results. Achieving such a network requires a social network of authors whose members are as nodes and directionless edges represent two authors with a joint article [6]. Bioinformatics is an interdisciplinary field that combines elements of computer science, information technology, mathematics, statistics, and biotechnology, providing methods for extracting information and biological processes for knowledge discovery [7]
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