论文标题
单细胞亚细胞蛋白使用新型深层建筑的新团体定位
Single-cell Subcellular Protein Localisation Using Novel Ensembles of Diverse Deep Architectures
论文作者
论文摘要
分解单个细胞内的蛋白质分布是理解其功能和状态的关键，并且对于开发新疗法是必不可少的。在这里，我们介绍杂交亚细胞蛋白质局部局部（HCPL），该蛋白质局部局部（HCPL）从弱标记的数据中学习，以稳健地定位单细胞亚细胞蛋白质模式。它包括创新的DNN体系结构利用小波过滤器，并学习了成功解决急剧细胞变异性的参数激活。 HCPL具有基于相关的新型体系结构的结合，可提高性能并有助于概括。我们的“ AI-Trains-ai”方法使大规模的数据注释可行，该方法决定了细胞的视觉完整性，并强调可靠的标签以进行有效的训练。在人类蛋白质地图集中，我们证明了HCPL在蛋白质定位模式的单细胞分类中定义了最新的。为了更好地了解HCPL的内部工作并评估其生物学相关性，我们分析了每个系统组件的贡献，并剖析了其定位预测的新兴特征。
Unravelling protein distributions within individual cells is key to understanding their function and state and indispensable to developing new treatments. Here we present the Hybrid subCellular Protein Localiser (HCPL), which learns from weakly labelled data to robustly localise single-cell subcellular protein patterns. It comprises innovative DNN architectures exploiting wavelet filters and learnt parametric activations that successfully tackle drastic cell variability. HCPL features correlation-based ensembling of novel architectures that boosts performance and aids generalisation. Large-scale data annotation is made feasible by our "AI-trains-AI" approach, which determines the visual integrity of cells and emphasises reliable labels for efficient training. In the Human Protein Atlas context, we demonstrate that HCPL defines state-of-the-art in the single-cell classification of protein localisation patterns. To better understand the inner workings of HCPL and assess its biological relevance, we analyse the contributions of each system component and dissect the emergent features from which the localisation predictions are derived.
