Abstract
Deep learning has recently enabled many advances for computer vision applications in image recognition, localization, segmentation, and understanding. However, applying deep learning models to a wider variety of domains is often limited by available labeled data. To address this problem, conventional approaches supplement more samples by augmenting existing datasets. However, these up-sampling methods usually only create derivations of the source images. To supplement with unique examples, we introduce an approach for generating purely synthetic data for object detection on biological pathway diagrams, which describe a series of molecular interactions leading to a certain biological function based on a set of rules and domain knowledge. Our method iteratively generates each pathway relationship uniquely. These realistic replicas improve the generalization significantly across a variety of settings. The code is available at https://github.com/JRunner97/Pathway_Data_Synthesis.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Girshick, R., et al.: Rich feature hierarchies for accu-rate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2014)
Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision (2015)
Ren, S., et al.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2016)
Lin, T.-Y., et al.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision (2017)
Lin, T.Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48
Shorten, C., Khoshgoftaar, T.M.: A survey on image data augmentation for deep learning. J. Big Data 6(1), 1–48 (2019). https://doi.org/10.1186/s40537-019-0197-0
Pinceti, A., Sankar, L., Kosut, O.: Synthetic Time-Series Load Data via Conditional Generative Adversarial Networks. arXiv preprint arXiv:2107.03545 (2021)
Wang, F., Zhuang, Y., Gu, H., Hu, H.: Automatic generation of synthetic LiDAR point clouds for 3-D data analysis. IEEE Trans. Instrum. Meas. 68(7), 2671–2673 (2019). https://doi.org/10.1109/TIM.2019.2906416
Griffiths, D., Boehm, J.: SynthCity: a large scale synthetic point cloud. arXiv preprint arXiv:1907.04758 (2019)
Kanehisa, M., Goto, S.: KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27–30 (2000)
He, K., et al.: Deep residual learning for image recog-nition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016)
Deng, J., et al.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Com-puter Vision and Pattern Recognition. IEEE (2009)
Cooley, J.W., Tukey, J.W.: An algorithm for the machine calculation of complex Fourier series. Math. Comput. 19(90), 297–301 (1965)
Acknowledgements
The research is supported by the National Library of Medicine of the National Institute of Health (NIH) award 5R01LM013392.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Thompson, J., Dong, H., Liu, K., He, F., Popescu, M., Xu, D. (2022). A Rule-Based Approach for Generating Synthetic Biological Pathways. In: Chicco, D., et al. Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2021. Lecture Notes in Computer Science(), vol 13483. Springer, Cham. https://doi.org/10.1007/978-3-031-20837-9_9
Download citation
DOI: https://doi.org/10.1007/978-3-031-20837-9_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-20836-2
Online ISBN: 978-3-031-20837-9
eBook Packages: Computer ScienceComputer Science (R0)