Proteins must be sorted to specific subcellular compartments to perform their functions. Abnormal protein subcellular localizations are related to many diseases. Although many efforts have been made in predicting protein subcellular localization from various static information, including sequences, structures and interactions, such static information cannot predict protein mis-localization events in diseases. On the contrary, the IHC (immunohistochemistry) images, which have been widely applied in clinical diagnosis, contains information that can be used to find protein mis-localization events in disease states. In this study, we create the Vislocas method, which is capable of finding mis-localized proteins from IHC images as markers of cancer subtypes. By combining CNNs and vision transformer encoders, Vislocas can automatically extract image features at both global and local level. Vislocas can be trained with full-sized IHC images from scratch. It is the first attempt to create an end-to-end IHC image-based protein subcellular location predictor. Vislocas achieved comparable or better performances than state-of-the-art methods. We applied Vislocas to find significant protein mis-localization events in different subtypes of glioma, melanoma and skin cancer. The mis-localized proteins, which were found purely from IHC images by Vislocas, are in consistency with clinical or experimental results in literatures. All codes of Vislocas have been deposited in a Github repository (). All datasets of Vislocas have been deposited in Zenodo ().

中文翻译：

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Vislocas：视觉转换器，用于从免疫组织化学图像中识别不同癌症亚型的蛋白质亚细胞错误定位特征

蛋白质必须分类到特定的亚细胞区室才能发挥其功能。蛋白质亚细胞定位异常与许多疾病有关。尽管在根据各种静态信息（包括序列、结构和相互作用）预测蛋白质亚细胞定位方面已经做出了许多努力，但此类静态信息无法预测疾病中的蛋白质错误定位事件。相反，广泛应用于临床诊断的IHC（免疫组织化学）图像包含可用于发现疾病状态下蛋白质错误定位事件的信息。在这项研究中，我们创建了 Vislocas 方法，该方法能够从 IHC 图像中找到错误定位的蛋白质作为癌症亚型的标记。通过结合 CNN 和视觉转换器编码器，Vislocas 可以自动提取全局和局部级别的图像特征。 Vislocas 可以从头开始使用全尺寸 IHC 图像进行训练。这是创建基于端到端 IHC 图像的蛋白质亚细胞位置预测器的首次尝试。 Vislocas 取得了与最先进方法相当或更好的性能。我们应用 Vislocas 发现了神经胶质瘤、黑色素瘤和皮肤癌不同亚型中显着的蛋白质错误定位事件。这些错误定位的蛋白质纯粹是从 Vislocas 的 IHC 图像中发现的，与文献中的临床或实验结果一致。 Vislocas的所有代码都已存放在Github存储库中（）。 Vislocas的所有数据集都已存放在Zenodo（）中。