An experimentally validated approach to automated biological evidence generation in drug discovery using knowledge graphs

Author:

Sudhahar SaatvigaORCID,Ozer BugraORCID,Chang Jiakang,Chadwick WayneORCID,O’Donovan DanielORCID,Campbell Aoife,Tulip Emma,Thompson Neil,Roberts IanORCID

Abstract

AbstractExplaining predictions for drug repositioning with biological knowledge graphs is a challenging problem. Graph completion methods using symbolic reasoning predict drug treatments and associated rules to generate evidence representing the therapeutic basis of the drug. Yet the vast amounts of generated paths that are biologically irrelevant or not mechanistically meaningful within the context of disease biology can limit utility. We use a reinforcement learning based knowledge graph completion model combined with an automatic filtering approach that produces the most relevant rules and biological paths explaining the predicted drug’s therapeutic connection to the disease. In this work we validate the approach against preclinical experimental data for Fragile X syndrome demonstrating strong correlation between automatically extracted paths and experimentally derived transcriptional changes of selected genes and pathways of drug predictions Sulindac and Ibudilast. Additionally, we show it reduces the number of generated paths in two case studies, 85% for Cystic fibrosis and 95% for Parkinson’s disease.

Publisher

Springer Science and Business Media LLC

Reference123 articles.

1. Ji, S., Pan, S., Cambria, E., Marttinen, P. & Philip, S. Y. A survey on knowledge graphs: representation, acquisition, and applications. IEEE Trans. Neural Netw. Learn. Syst. 33, 494–514 (2021).

2. Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J. & Yakhnenko, O. Translating embeddings for modeling multi-relational data. Adv. Neural Inf. Process. Syst. 26, 1–9 (2013).

3. Wang, Z., Zhang, J., Feng, J. & Chen, Z. Knowledge graph embedding by translating on hyperplanes. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 28 (AAAI, 2014).

4. Sun, Z., Deng, Z. H., Nie, J. Y., & Tang, J. RotatE: knowledge graph embedding by relational rotation in complex space. In Proceedings of the International Conference on Learning Representations (ICLR, 2019).

5. Nickel, M., Tresp, V. & Kriegel, H. P. A three-way model for collective learning on multi-relational data. Proc. ICML 11, 3104482–3104584 (2011).

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3