Tang C. (coauthor). 12/3/2018. “
Predicting Disease-Related Associations by Heterogeneous Network Embedding.” In 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Pp. 548-555. Madrid, Spain: IEEE.
AbstractElucidating biological mechanisms underlying complex diseases is an important goal in biomedical research. Recent advances in biological technology have enabled the generation of massive volume of data in genomics, transcriptomics, proteomics, epigenomics, metagenomics, metabolomics, nutriomics, etc., leading to the emergence of systems biology approach to investigating complex diseases. However, most of the data remain underutilized after their initial acquisition and analysis. There is a growing gap between the generation of the multifaceted data and our ability to integrate and analyze them. Inspired by the observation that many of the aforementioned data can be represented by networks, we propose a networkbased model to encapsulate the rich information provided in each database and to connect across different databases. We integrate several public databases to construct a heterogeneous network in which nodes are entities such as genes, miRNAs, diseases, and edges represent known relationships between them. One fundamental challenge is how to perform meaningful analysis on such network, overcoming the intrinsic heterogeneity. We propose a network embedding method to learn a low-dimensional vector space that best preserves the known relationships between entities. Based on the learned vector representations, entities that are close to each other but currently do not have known direct connections, are likely to have an association and therefore are good candidates for future investigation. In the experiments, we construct a heterogeneous network of genes, miRNAs and diseases using data from six public databases. To evaluate the performance of the proposed method, we predict disease-gene and disease-miRNA associations. Comparison of our novel method with several state-of-the-art methods clearly demonstrates the advantage of our method, as it is the only one that takes full advantage of the rich contextual information provided by the heterogeneous network. The encouraging results suggest that our method can provide help in identifying new hypotheses to guide future research.
B521.pdf Tang C. (first author). 11/22/2018. “
Rethinking Data Sharing at the Dawn of a Health Data Economy: A Viewpoint.” J Med Internet Res, 20, 11, Pp. e11519.
Publisher's VersionAbstract
A healthcare data economy has begun to form, but its rise has been tempered by the profound lack of sharing of both data and data products such as models, intermediate results, and annotated training corpses, and this severely limits the potential for triggering economic cluster effects. Economic cluster effects represent a means to elicit benefit from economies of scale from internal data innovations and are beneficial because they may mitigate challenges from external sources. Within institutions, data product sharing is needed to spark data entrepreneurship and data innovation, and cross-institutional sharing is also critical especially for rare conditions.
