Clinical trial recommendations using Semantics-Based inductive inference and knowledge graph embeddings
Open accessAbstract
Objective Designing a new clinical trial entails many decisions, such as defining a cohort and setting the study objectives to name a few, and therefore can benefit from recommendations based on exhaustive mining of past clinical trial records. This study proposes an approach based on knowledge graph embeddings and semantics-driven inductive inference for generating such recommendations.Method The proposed recommendation methodology is based on neural embeddings trained on first-of-its-kind knowledge graph constructed from clinical trials data. The methodology includes design of a knowledge graph for clinical trial data, evaluation of various knowledge graph embedding techniques for it, application of a novel inductive inference method using these embeddings, and generation of recommendations for clinical trial design. The study uses freely available data from clinicaltrials.gov and related sources.Results The proposed approach for recommendations obtained relevance scores ranging from 70% to 83%. These scores were determined by evaluating the text similarity of recommended elements to actual elements used in clinical trials that are in progress. Furthermore, the most pertinent recommendations were consistently located towards the top of the list, indicating the effectiveness of our method.Conclusion Our study suggests that inductive inference using node semantics is a viable approach for generating recommendations using graphs neural embeddings, and that there is a potential for improvement in training graph embeddings using node semantics.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Domain-specific LLMs perform well in clinical text summarization, note generation, and medical question answering.
“Domain-specific LLMs, fine-tuned on medical corpora or using knowledge graph embeddings, perform well in clinical text summarization [10, 11], note generation [12, 13], and medical question answering [14].”
From Enhancing hepatopathy clinical trial efficiency: a secure, large language model-powered pre-screening pipeline (2025) · cites this paper as [11]
Cites
Graph Attention NetworksPreprint
Veličković, P., Casanova, A., Liò, P., Cucurull, G., Romero, A., & Bengio, Y. (2017) · arXiv (Cornell University) · doi:10.17863/cam.48429 · Cited by 12,099 · see citing works
node2vec: Scalable Feature Learning for Networks
Grover, A., & Leskovec, J. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939754 · Cited by 10,926 · see citing works
DrugBank 5.0: a major update to the DrugBank database for 2018
Wishart, D. S., Feunang, Y. D., Guo, A. C., Lo, E. J., Marcu, A., Grant, J. R., Sajed, T., Johnson, D., Li, C., Sayeeda, Z., et al. (2018) · Nucleic Acids Research · doi:10.1093/nar/gkx1037 · Cited by 8,404 · see citing works
Graph neural networks: A review of methods and applications
Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., & Sun, M. (2020) · AI Open · doi:10.1016/j.aiopen.2021.01.001 · Cited by 6,880 · see citing works
(2013) · Cited by 5,726 · see citing works
Modeling Relational Data with Graph Convolutional Networks
Schlichtkrull, M., Kipf, T. N., Bloem, P., van den Berg, R., Titov, I., & Welling, M. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-93417-4_38 · Cited by 5,570 · see citing works
Learning Entity and Relation Embeddings for Knowledge Graph Completion
Lin, Y., Liu, Z., Sun, M., Liu, Y., & Zhu, X. (2015) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v29i1.9491 · Cited by 3,749 · see citing works
Knowledge Graph Embedding: A Survey of Approaches and Applications
Wang, Q., Mao, Z., Wang, B., & Guo, L. (2017) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2017.2754499 · Cited by 2,654 · see citing works
Embedding Entities and Relations for Learning and Inference in Knowledge Bases
Yang, B., Yih, W.-T., He, X., Gao, J., & Deng, L. (2014) · arXiv · doi:10.48550/arxiv.1412.6575 · Cited by 2,252 · see citing works
Complex Embeddings for Simple Link Prediction
Trouillon, T., Welbl, J., Riedel, S., Gaussier, É., & Bouchard, G. (2016) · arXiv · doi:10.48550/arxiv.1606.06357 · Cited by 2,100 · see citing works
A Three-Way Model for Collective Learning on Multi-Relational Data
Nickel, M., Tresp, V., & Kriegel, H. (2011) · Cited by 1,806 · see citing works
Mutation nomenclature extensions and suggestions to describe complex mutations: A discussion
den Dunnen, J. T., & Antonarakis, S. E. (2000) · Human Mutation · doi:10.1002/(sici)1098-1004(200001)15:1 · Cited by 1,746 · see citing works
Asymmetric Transitivity Preserving Graph Embedding
Ou, M., Cui, P., Pei, J., Zhang, Z., & Zhu, W. (2016) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2939672.2939751 · Cited by 1,298 · see citing works
A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network
Nguyen, D. Q., Nguyen, T. D., Nguyen, D. Q., & Phung, D. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers) · doi:10.18653/v1/n18-2053 · Cited by 923 · see citing works
Wang, Y., Zhang, S., Li, F., Zhou, Y., Zhang, Y., Wang, Z., Zhang, R., Zhu, J., Ren, Y., Tan, Y. C., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz981 · Cited by 794 · see citing works
Bio2RDF: Towards a mashup to build bioinformatics knowledge systems
Belleau, F., Nolin, M.-A., Tourigny, N., Rigault, P., & Morissette, J. (2008) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2008.03.004 · Cited by 787 · see citing works
Building a knowledge graph to enable precision medicine
Chandak, P., Huang, K., & Zitnik, M. (2023) · Scientific Data · doi:10.1038/s41597-023-01960-3 · Cited by 522 · see citing works
PharmGKB: the Pharmacogenetics Knowledge Base
Hewett, M. (2002) · Nucleic Acids Research · doi:10.1093/nar/30.1.163 · Cited by 430 · see citing works
A knowledge graph to interpret clinical proteomics data
Santos, A., Colaço, A. R., Nielsen, A. B., Niu, L., Strauss, M. T., Geyer, P. E., Coscia, F., Albrechtsen, N. J. W., Mundt, F. O., Jensen, L. J., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01145-6 · Cited by 345 · see citing works
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 · see citing works
Discovering protein drug targets using knowledge graph embeddings
Mohamed, S. K., Nováček, V., & Nounu, A. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz600 · Cited by 227 · see citing works
Knowledge Graph Embedding: A Survey from the Perspective of Representation Spaces
Cao, J., Fang, J., Meng, Z., & Liang, S. (2024) · ACM Computing Surveys · doi:10.1145/3643806 · Cited by 188 · see citing works
PharmKG: a dedicated knowledge graph benchmark for bomedical data mining
Zheng, S., Rao, J., Ying, S., Zhang, J., Xiao, X., Fang, E. F., Yang, Y., & Niu, Z. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa344 · Cited by 166 · see citing works
Neuro-symbolic representation learning on biological knowledge graphs
Alshahrani, M., Khan, M. A., Maddouri, O., Kinjo, A. R., Queralt-Rosiñach, N., & Hoehndorf, R. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx275 · Cited by 161 · see citing works
A global network of biomedical relationships derived from text
Percha, B., & Altman, R. B. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty114 · Cited by 131 · see citing works
Ali, M., Berrendorf, M., Hoyt, C. T., Vermue, L., Galkin, M., Sharifzadeh, S., Fischer, A., Tresp, V., & Lehmann, J. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3124805 · Cited by 125 · see citing works
Abu-Salih, B., Al‐Qurishi, M., Alweshah, M., AL-Smadi, M., Alfayez, R., & Saadeh, H. (2023) · Journal Of Big Data · doi:10.1186/s40537-023-00774-9 · Cited by 121 · see citing works
Fundamentals of clinical trial design
Evans, S. R. (2010) · Journal of Experimental Stroke and Translational Medicine · doi:10.6030/1939-067x-3.1.19 · Cited by 119 · see citing works
Sosa, D. N., Derry, A., Guo, M. G., Wei, E., Brinton, C., & Altman, R. B. (2019) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789811215636_0041 · Cited by 110 · see citing works
PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph EmbeddingsPreprint
Ali, M., Berrendorf, M., Hoyt, C. T., Vermue, L., Sharifzadeh, S., Tresp, V., & Lehmann, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.14175 · Cited by 84 · see citing works
Large Language Models and Knowledge Graphs: Opportunities and Challenges
Pan, J. Z., Razniewski, S., Kalo, J.-C., Singhania, S., Chen, J., Dietze, S., Jabeen, H., Omeliyanenko, J., Zhang, W. C., Lissandrini, M., et al. (2023) · UvA-DARE (University of Amsterdam) · doi:10.4230/tgdk.1.1.2 · Cited by 84 · see citing works
Knowledge graph quality control: A survey
Wang, X., Chen, L., Ban, T., Usman, M., Guan, Y., Liu, S., Wu, T., & Chen, H. (2021) · Fundamental Research · doi:10.1016/j.fmre.2021.09.003 · Cited by 81 · see citing works
OpenBioLink: a benchmarking framework for large-scale biomedical link prediction
Breit, A., Ott, S., Agibetov, A., & Matthias Samwald, A. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa274 · Cited by 73 · see citing works
Ren, Z.-H., You, Z., Yu, C.-Q., Li, L., Guan, Y.-J., Guo, L.-X., & Pan, J. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac363 · Cited by 72 · see citing works
Hong, L., Lin, J., Li, S., Wan, F., Yang, H. M., Jiang, T., Zhao, D. Y., & Zeng, J. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0189-y · Cited by 66 · see citing works
Inductive Relation Prediction by Subgraph ReasoningPreprint
Teru, K. K., Denis, E., & Hamilton, W. L. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.06962 · Cited by 62 · see citing works
PecanPy: a fast, efficient and parallelized Python implementation of node2vec
Liu, R., & Krishnan, A. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab202 · Cited by 61 · see citing works
Fernández-Torras, A., Duran-Frigola, M., Bertoni, M., Locatelli, M., & Aloy, P. (2022) · Nature Communications · doi:10.1038/s41467-022-33026-0 · Cited by 59 · see citing works
Drug knowledge bases and their applications in biomedical informatics research
Zhu, Y., Elemento, O., Pathak, J., & Wang, F. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbx169 · Cited by 49 · see citing works
NodePiece: Compositional and Parameter-Efficient Representations of Large Knowledge GraphsPreprint
Galkin, M., Denis, E., Wu, J., & Hamilton, W. L. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.12144 · Cited by 47 · see citing works
Biomedical discovery through the integrative biomedical knowledge hub (iBKH)
Su, C., Hou, Y., Zhou, M., Rajendran, S., Maasch, J. R. M. A., Abedi, Z., Zhang, H., Bai, Z., Cuturrufo, A., Guo, W. L., et al. (2023) · iScience · doi:10.1016/j.isci.2023.106460 · Cited by 34 · see citing works
Contrastive Learning with Generated Representations for Inductive Knowledge Graph Embedding
Li, Q., Joty, S., Wang, D., Shi, F., Zhang, Y., & Qin, C. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.900 · Cited by 11 · see citing works
Query Structure Modeling for Inductive Logical Reasoning Over Knowledge Graphs
Wang, S., Wei, Z., Han, M., Fan, Z., Shan, H., Zhang, Q., & Huang, X. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.259 · Cited by 10 · see citing works
Knowledge Graph Embedding: A Survey from the Perspective of Representation SpacesPreprint
Cao, J., Fang, J., Meng, Z., et al. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2211.03536 · Cited by 7 · see citing works
Flagged first, then by citations
4 of the 48 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Saady, M., Eissa, M., Yacoub, A. S., Hamed, A. B., & Azzazy, H. M. E.-S. (2025) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2025.103066 · Cited by 15 · see citing works
Integrating LLMs and Knowledge Graphs for Medical AI: Advances, Challenges, and Future Directions
Murali, L., Gopakumar, G., Viswanathan, D. M., Raghu Raman, R., & Nedungadi, P. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3622058 · Cited by 19 · see citing works
CPRS: a clinical protocol recommendation system based on LLMs
Ruan, J., Su, Q., Chen, Z., Huang, J., & Li, Y. (2024) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2024.105746 · Cited by 9 · see citing works
Gui, X., Lv, H., Wang, X., Lv, L., Xiao, Y., & Wang, L. (2025) · BioData Mining · doi:10.1186/s13040-025-00458-5 · Cited by 7 · see citing works
Recommending physicians with multimodal data and medical knowledge graph on healthcare platforms
Deng, W., Zhu, P., Chen, H., Liu, Z., & Feng, G. (2024) · Journal of Information Science · doi:10.1177/01655515241281828 · Cited by 1 · see citing works
Artificial Intelligence in Clinical Trials: From Protocol Design to Pharmacovigilance
Schulman, A., Fernández‐Torras, A., Gadiya, Y., Saeed, K., Mestres, J., & Tanoli, Z. (2026) · Springer eBooks · doi:10.1007/978-3-031-98022-0_24 · Cited by 1 · see citing works
Zhou, Z., Liu, Y., Xie, Y., Wang, B., Yang, X., & Feng, Z. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791724 · Cited by 3 · see citing works
Artemis: Harnessing Knowledge Graphs for Next-Generation Drug Target Prioritization
Kiselev, V. Y., & Ainscow, E. (2026) · Computational and Structural Biotechnology Journal · doi:10.34133/csbj.0130
Shruthi, S., & Naidu, D. H. R. (2026) · Awareness Journals · doi:10.67277/awj01-2026-000010
- Authors
- Devarakonda, M. V., Mohanty, S., Sunkishala, R. R., Mallampalli, N., & Liu, X.
- Corresponding author
- Devarakonda, Murthy V.
- Venue
- Journal of Biomedical Informatics
- Publisher
- Elsevier BV
- Published
- 2024-06
- Volume
- 154
- Pages
- 104627
- ISSN
- 1532-0464 (ISSN-L)
- OpenAlex
- W4393339911
- Cited by
- 9
- References
- 48
- Citations by year
- 2026: 4 · 2025: 3 · 2024: 2
- FWCI
- 3.39
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (vor, 365 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Journal of Biomedical Informatics (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by-nc-nd)
- The Novartis Repository (Novartis) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- clinical trial design, recommendation generation, knowledge graph embedding, inductive reasoning, ClinicalTrials.gov, text similarity
- MeSH terms
- Algorithms, Clinical Trials as Topic, Humans, Research Design, Semantics, Neural Networks, Computer, Data Mining
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
