Department of Quantitative Health Sciences
Bioinformatics | Biomedical | Biostatistics | Medicine and Health Sciences | Statistics and Probability
Biomedical research is increasingly collaborative, and successful collaborations often produce high impact work. Computational approaches can be developed for automatically predicting biomedical research collaborations. Previous works of collaboration prediction mainly explored the topological structures of research collaboration networks, leaving out rich semantic information from the publications themselves. In this paper, we propose supervised machine learning approaches to predict research collaborations in the biomedical field. We explored both the semantic features extracted from author research interest profile and the author network topological features. We found that the most informative semantic features for author collaborations are related to research interest, including similarity of out-citing citations, similarity of abstracts. Of the four supervised machine learning models (naive Bayes, naive Bayes multinomial, SVMs, and logistic regression), the best performing model is logistic regression with an ROC ranging from 0.766 to 0.980 on different datasets. To our knowledge we are the first to study in depth how research interest and productivities can be used for collaboration prediction. Our approach is computationally efficient, scalable and yet simple to implement. The datasets of this study are available at https://github.com/qingzhanggithub/medline-collaboration-datasets.
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DOI of Published Version
PLoS One. 2014 Nov 6;9(11):e111795. doi: 10.1371/journal.pone.0111795. eCollection 2014. Link to article on publisher's site
Zhang Q, Yu H. (2014). Computational approaches for predicting biomedical research collaborations. Open Access Publications by UMMS Authors. https://doi.org/10.1371/journal.pone.0111795. Retrieved from https://escholarship.umassmed.edu/oapubs/2469
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This work is licensed under a Creative Commons 1.0 Public Domain Dedication.