MOTIVATION: Graph embedding learning that aims to automatically learn low-dimensional node representations, has drawn increasing attention in recent years. To date, most recent graph embedding methods are evaluated on social and information networks and are not comprehensively studied on biomedical networks under systematic experiments and analyses. On the other hand, for a variety of biomedical network analysis tasks, traditional techniques such as matrix factorization (which can be seen as a type of graph embedding methods) have shown promising results, and hence there is a need to systematically evaluate the more recent graph embedding methods (e.g. random walk-based and neural network-based) in terms of their usability and potential to further the state-of-the-art. RESULTS: We select 11 representative graph embedding methods and conduct a systematic comparison on 3 important biomedical link prediction tasks: drug-disease association (DDA) prediction, drug-drug interaction (DDI) prediction, protein-protein interaction (PPI) prediction; and 2 node classification tasks: medical term semantic type classification, protein function prediction. Our experimental results demonstrate that the recent graph embedding methods achieve promising results and deserve more attention in the future biomedical graph analysis. Compared with three state-of-the-art methods for DDAs, DDIs and protein function predictions, the recent graph embedding methods achieve competitive performance without using any biological features and the learned embeddings can be treated as complementary representations for the biological features. By summarizing the experimental results, we provide general guidelines for properly selecting graph embedding methods and setting their hyper-parameters for different biomedical tasks. AVAILABILITY AND IMPLEMENTATION: As part of our contributions in the paper, we develop an easy-to-use Python package with detailed instructions, BioNEV, available at: https://github.com/xiangyue9607/BioNEV, including all source code and datasets, to facilitate studying various graph embedding methods on biomedical tasks. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Unstructured clinical texts contain rich health-related information. To better utilize the knowledge buried in clinical texts, discovering synonyms for a medical query term has become an important task. Recent automatic synonym discovery methods leveraging raw text information have been developed. However, to preserve patient privacy and security, it is usually quite difficult to get access to large-scale raw clinical texts. In this paper, we study a new setting named synonym discovery on privacy-aware clinical data (i.e., medical terms extracted from the clinical texts and their aggregated co-occurrence counts, without raw clinical texts). To solve the problem, we propose a new framework SurfCon that leverages two important types of information in the privacy-aware clinical data, i.e., the surface form information, and the global context information for synonym discovery. In particular, the surface form module enables us to detect synonyms that look similar while the global context module plays a complementary role to discover synonyms that are semantically similar but in different surface forms, and both allow us to deal with the OOV query issue (i.e., when the query is not found in the given data). We conduct extensive experiments and case studies on publicly available privacy-aware clinical data, and show that SurfCon can outperform strong baseline methods by large margins under various settings.

Objective: Point of care access to knowledge from full text journal articles supports decision making and decreases medical errors. However, it is an overwhelming task to obtain full text for all journals and the quality of information for a specific topic such as Congestive Heart Failure (CHF) varies across the journals. In this paper, we develop a method to automatically rate journals for a given clinical topic to enable filtering journals or ranking the articles based on source journal. Materials and Methods: We surveyed 169 cardiologists chosen across the US that practice medicine and publish on the topic of CHF. They provided subjective opinion on how valuable the information provided in 60 journals (chosen by Mayo Clinic cardiologists) is for their clinical decision-making. Results: We identified the top CHF journals chosen by cardiologists and the objective metrics that correlate with their scores. Our best Multiple Linear Regression model has a correlation of 0.880 based on five-fold cross-validation. Discussion: We demonstrated that general journal metrics such as impact factor, h-index and number of articles per year provide better results when used in combination with topic-specific metrics such as number of abstracts indexed with the corresponding MeSH® terms. Conclusion: We obtained a journal priority score formula based on different journal metrics to automatically rate any journal in relation to its perceived importance in CHF. Our study shows that using this formula might yield a reliable list of prioritized journals even across other clinical topics such as Multiple Sclerosis.