Flowering Time Prediction of Wheat From DIA-MS Data

Traditional methods utilising data-independent acquisition mass spectrometry (DIA-MS) data for predictions depend on database searches against predefined spectral libraries for characterisation and quantification of the proteomes, limiting scalability and adaptability across various applications. However, directly applying existing networks on DIA-MS data represented as images for end-to-end predictions struggles to mine a predictive pattern, due to non-uniform region importance across the image and divergences exhibited among different regions of the image. To overcome these limitations, we propose a new frame-work with two modules: i) a dynamic sampling module that identifies regions of interest from the DIA-MS image, constraining the network to focus on the most informative regions of the image only; ii) a mixture of experts module that sparsely routes the regions of interest to related expert networks, facilitating adaptive computation of region features. The region features are then fused for predictions. Experimentally, to benchmark our method, we collected a large DIA-MS dataset of wheat for flowering time prediction, and our approach significantly outperforms previous end-to-end methods, i. e., 0.171 R2 improvements.