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Erschienen in:
2022 | OriginalPaper | Buchkapitel
A Rule-Based Approach for Generating Synthetic Biological Pathways
verfasst von : Joshua Thompson, Haoyu Dong, Kai Liu, Fei He, Mihail Popescu, Dong Xu
Erschienen in: Computational Intelligence Methods for Bioinformatics and Biostatistics
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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.