The DOI matches its Crossref record; 197 of the 197 references it cites were found in a registry or an index.
Constructing knowledge graphs and their biomedical applications
Open accessAbstract
Knowledge graphs can support many biomedical applications. These graphs represent biomedical concepts and relationships in the form of nodes and edges. In this review, we discuss how these graphs are constructed and applied with a particular focus on how machine learning approaches are changing these processes. Biomedical knowledge graphs have often been constructed by integrating databases that were populated by experts via manual curation, but we are now seeing a more robust use of automated systems. A number of techniques are used to represent knowledge graphs, but often machine learning methods are used to construct a low-dimensional representation that can support many different applications. This representation is designed to preserve a knowledge graph's local and/or global structure. Additional machine learning methods can be applied to this representation to make predictions within genomic, pharmaceutical, and clinical domains. We frame our discussion first around knowledge graph construction and then around unifying representational learning techniques and unifying applications. Advances in machine learning for biomedicine are creating new opportunities across many domains, and we note potential avenues for future work with knowledge graphs that appear particularly promising.
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Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Efficient Estimation of Word Representations in Vector Space
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1301.3781 · Cited by 26,568
Distributed Representations of Words and Phrases and their Compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1310.4546 · Cited by 24,533
Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387
Auto-Encoding Variational Bayes
Kingma, D. P., & Welling, M. (2013) · arXiv · doi:10.48550/arxiv.1312.6114 · Cited by 20,067
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570
A Global Geometric Framework for Nonlinear Dimensionality Reduction
Tenenbaum, J. B., Silva, V. D., & Langford, J. C. (2000) · Science · doi:10.1126/science.290.5500.2319 · Cited by 13,585
(2016) · Cited by 11,679
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Graph representation learning in biomedicine and healthcare
Li, M. M., Huang, K., & Zitnik, M. (2022) · Nature Biomedical Engineering · doi:10.1038/s41551-022-00942-x · Cited by 294
“…between genes 1 ; edges between genes and diseases can indicate genes that are ‘upregulated by’, ‘downregulated by’ or ‘associated with’ a disease 2 ; and edges in a knowledge network built from electronic health records (EHRs) can indicate co-occurrences of medical codes across patients 3–5 .”
Toward better drug discovery with knowledge graph
Zeng, X., Tu, X., Liu, Y., Fu, X., & Su, Y. (2022) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2021.09.003 · Cited by 229
How successful are AI-discovered drugs in clinical trials? A first analysis and emerging lessons
KP Jayatunga, M., Ayers, M., Bruens, L., Jayanth, D., & Meier, C. (2024) · Drug Discovery Today · doi:10.1016/j.drudis.2024.104009 · Cited by 166
Multimodal learning with graphs
Ektefaie, Y., Dasoulas, G., Noori, A., Farhat, M., & Zitnik, M. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00624-6 · Cited by 166
Murali, L., Gopakumar, G., Viswanathan, D. M., & Nedungadi, P. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104403 · Cited by 93
Metadata
- Authors
- Nicholson, D. N., & Greene, C. S.
- Venue
- Computational and Structural Biotechnology Journal
- Published
- 2020
- Volume
- 18
- Pages
- 1414-1428
- ISSN
- 2001-0370 (ISSN-L)
- OpenAlex
- W3033896008
- Cited by
- 299
- References
- 197
- FWCI
- 9.47
- Type
- journal article
- Language
- en
- Open access
- gold
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