Prediction of Disease-Gene Associations by an Ensemble of Knowledge Graph Completion

Ruhina Karani; Jay Mehta; Jay Mistry; Harit Koladia; Chetashri Bhadane

doi:10.17485/IJST/v17i11.83

Article

Prediction of Disease-Gene Associations by an Ensemble of Knowledge Graph Completion

VIEWS 165
PDF 64

Indian Journal of Science and Technology

DOI: 10.17485/IJST/v17i11.83

Year: 2024, Volume: 17, Issue: 11, Pages: 1078-1086

Original Article

Prediction of Disease-Gene Associations by an Ensemble of Knowledge Graph Completion

Ruhina Karani^1*, Jay Mehta¹, Jay Mistry¹, Harit Koladia¹, Chetashri Bhadane¹

¹Department of Computer Engineering, Dwarkadas J. Sanghvi College of Engineering, Mumbai, Maharashtra, India

*Corresponding Author
Email: [email protected]

Received Date:12 January 2024, Accepted Date:12 February 2024, Published Date:07 March 2024

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objective: This research aims to enhance genetic counselors' efficiency in analyzing genetic data across diverse medical settings, spanning prenatal scans, tumor testing, carrier typing, and Fluorescence In Situ Hybridization (FISH), etc. The objective is to employ graph-based techniques for identifying potential gene-disease associations and recommending personalized medical interventions. The application scope extends to areas like personalized medicine, newborn screening, and genetic probing. Methods: The study utilizes a novel technique within the PrimeKG genetic graph database, predicting gene-disease associations. Ensembles are constructed from six models - TransE, TransD, TransR, TransH, ComplEx, and DistMult. The hits@10 metric evaluates the model's effectiveness, measuring the accuracy of predictions within the top 10 ranked associations. The ensemble achieves a score of 0.52, indicating a significant proportion of correct predictions in the top ten associations. Findings: The research presents an efficient approach to identify gene-disease associations, demonstrating a high hits@10 metric accuracy (0.52). This method significantly aids medical professionals in making informed patient care decisions, with potential applications in prenatal scans, tumor testing, carrier typing, and more. The findings underscore the utility of graph-based techniques in transforming disease identification and treatment through genetic data analysis. Novelty: This study introduces a unique approach, leveraging ensembles from diverse models, to predict gene-disease associations within the PrimeKG genetic graph database. The hits@10 metric underscores the model's efficiency, presenting a novel and valuable contribution to the field of genetic data analysis and healthcare decision-making.

Keywords: Gene Disease Prediction, Machine Learning, Knowledge Graph Completion, Drug Discovery, Healthcare

References

Roman-Naranjo P, Parra-Perez AM, Lopez-Escamez JA. A systematic review on machine learning approaches in the diagnosis and prognosis of rare genetic diseases. Journal of Biomedical Informatics. 2023;143:1–8. Available from: https://doi.org/10.1016/j.jbi.2023.104429
Kim K, Yoo D, Lee HS, Lee KJ, Park SB, Kim C, et al. Identification of potential biomarkers for diagnosis of pancreatic and biliary tract cancers by sequencing of serum microRNAs. BMC Medical Genomics. 2019;12(1):1–11. Available from: https://doi.org/10.1186/s12920-019-0521-8
Poirion OB, Jing Z, Chaudhary K, Huang S, Garmire LX. DeepProg: an ensemble of deep-learning and machine-learning models for prognosis prediction using multi-omics data. Genome Medicine. 2021;13(1):1–15. Available from: https://doi.org/10.1186/s13073-021-00930-x
Jafari S, Ravan M, Karimi-Sani I, Aria H, Hasan-Abad AM, Banasaz B, et al. Screening and identification of potential biomarkers for pancreatic cancer: An integrated bioinformatics analysis. Pathology - Research and Practice. 2023;249:154726. Available from: https://doi.org/10.1016/j.prp.2023.154726
Chandak P. PrimeKG (Version 2) 2022. Available from: https://doi.org/10.7910/DVN/IXA7BM
Wang Z, Gu Y, Zheng S, Yang L, Li J. MGREL: A multi-graph representation learning-based ensemble learning method for gene-disease association prediction. Computers in Biology and Medicine. 2023;155:106642. Available from: https://doi.org/10.1016/j.compbiomed.2023.106642
Lei X, Zhang Y. Predicting disease-genes based on network information loss and protein complexes in heterogeneous network. Information Sciences. 2019;479:386–400. Available from: https://doi.org/10.1016/j.ins.2018.12.008
Brusa M, Dickenson D. Personalized medicine and genetic newborn screening. In: Barilan YM, Brusa M, Ciechanover A., eds. Can precision medicine be personal; Can personalized medicine be precise?. (pp. 107-C8.P56) Oxford University Press. 2022.
Chandak P, Huang K, Zitnik M. Building a knowledge graph to enable precision medicine. Scientific Data. 2023;10(1):1–16. Available from: https://doi.org/10.1038/s41597-023-01960-3
Chen CHH, Tanaka K, Kotera M, Funatsu K. Comparison and improvement of the predictability and interpretability with ensemble learning models in QSPR applications. Journal of Cheminformatics. 2020;12(1):1–16. Available from: https://doi.org/10.1186/s13321-020-0417-9
Dogan A, Birant D. A Weighted Majority Voting Ensemble Approach for Classification. In: 2019 4th International Conference on Computer Science and Engineering (UBMK). IEEE. 2019.
Ratajczak F, Joblin M, Hildebrandt M, Ringsquandl M, Falter-Braun P, Heinig M. Speos: an ensemble graph representation learning framework to predict core gene candidates for complex diseases. Nature Communications. 2023;14(1):1–18. Available from: https://doi.org/10.1038/s41467-023-42975-z
Qian W, Fu C, Zhu Y, Cai D, He X. Translating Embeddings for Knowledge Graph Completion with Relation Attention Mechanism. In: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence. (pp. 4286-4292) International Joint Conferences on Artificial Intelligence Organization. 2018.
Asmara SM, Sahabudin NA, Ismail NSN, Sabri IAA. A Review of Knowledge Graph Embedding Methods of TransE, TransH and TransR for Missing Links. In: 2023 IEEE 8th International Conference On Software Engineering and Computer Systems (ICSECS). (pp. 470-475) IEEE. 2023.
Wu D, Zhao J, Li M, Shi M. A Knowledge Representation Method for Multiple Pattern Embeddings Based on Entity-Relation Mapping Matrix. In: 2022 International Joint Conference on Neural Networks (IJCNN). (pp. 1-8) IEEE. 2022.
Shen T, Zhang F, Cheng J. A comprehensive overview of knowledge graph completion. Knowledge-Based Systems. 2022;255. Available from: https://doi.org/10.1016/j.knosys.2022.109597
Mohamed SK, Muñoz E, Novacek V. On Training Knowledge Graph Embedding Models. Information. 2021;12(4):1–19. Available from: https://doi.org/10.3390/info12040147
Liu Y, Tian J, Liu X, Tao T, Ren Z, Wang X, et al. Research on a Knowledge Graph Embedding Method Based on Improved Convolutional Neural Networks for Hydraulic Engineering. Electronics. 2023;12(14):1–16. Available from: https://doi.org/10.3390/electronics12143099
Gao Z, Pan Y, Ding P, Xu R. A knowledge graph-based disease-gene prediction system using multi-relational graph convolution networks. AMIA Annual Symposium Proceedings. 2023;2022:468–476. Available from: https://pubmed.ncbi.nlm.nih.gov/37128437/

Copyright

© 2024 Karani et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)