Abstract
Improving the performance of deep learning algorithms and reducing the cost of training time are ongoing challenges in bioinformatics. Some strategies are proposed to address these challenges such as changing learning rate and early stopping technique together with cross-validation training. These approaches still take plenty of training time and have some bottlenecks in improving performance under traditional cross-validation training settings. In this study, we propose a successive cross-validation training strategy for biomedical data and develop a new training scheme to improve performance with reduced training time using weight transfer learning. We design and perform multiple experiments with three different domains to evaluate the proposed training scheme. The deep learning models include DeepDTA for drug-target affinity prediction, RAAU-Net for glioma segmentation, and DeepCaps for image classification. Experimental results demonstrate that our proposed training scheme not only outperforms the existing scheme on both performance and efficiency in bioinformatics but also can be easily generalized to a variety of intelligent tasks.
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Acknowledgments
This work is funded partially by the National Natural Science Foundation of China under Grant No. 61802442, No. 61877059, the Natural Science Foundation of Hunan Province under Grant No. 2019JJ50775, the 111 Project (No. B18059), the Hunan Provincial Science and Technology Program (No. 2018WK4001), and the Hunan Provincial Science and Technology Innovation Leading Plan (No. 2020GK2019).
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Cheng, J., Zhao, Q., Xu, L., Liu, J. (2021). A New Deep Learning Training Scheme: Application to Biomedical Data. In: Wei, Y., Li, M., Skums, P., Cai, Z. (eds) Bioinformatics Research and Applications. ISBRA 2021. Lecture Notes in Computer Science(), vol 13064. Springer, Cham. https://doi.org/10.1007/978-3-030-91415-8_38
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DOI: https://doi.org/10.1007/978-3-030-91415-8_38
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