Increasingly, multiple parallel omics datasets are collected from biological samples. Integrating these datasets for classification is an open area of research. Additionally, whilst multiple datasets may be available for the training samples, future samples may only be measured by a single technology requiring methods which do not rely on the presence of all datasets for sample prediction. This enables us to directly compare the protein and the gene profiles. New samples with just one set of measurements (e.g., just protein) can then be mapped to this latent common space where classification is performed. Using this approach, we achieved an improvement of up to 12 percent in accuracy when classifying samples based on their protein measurements compared with baseline methods which were trained on the protein data alone. We illustrate that the additional inclusion of the gene expression or protein expression in the training process enabled the separation between the classes to become clearer.

中文翻译：

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乳腺癌亚型IHC分类的异构域适应。

越来越多地从生物学样本中收集了多个并行组学数据集。整合这些数据集进行分类是一个开放的研究领域。另外，尽管多个数据集可用于训练样本，但未来的样本只能通过一种技术来测量，该技术需要不依赖于所有数据集进行样本预测的方法。这使我们能够直接比较蛋白质和基因图谱。然后可以将仅具有一组测量值的新样本（例如，仅蛋白质）映射到执行分类的潜在公共空间。使用这种方法，与仅根据蛋白质数据进行训练的基线方法相比，根据蛋白质测量结果对样品进行分类时，我们的准确度提高了12％。