In the complex landscape of hematologic samples such as peripheral blood or
bone marrow derived from flow cytometry (FC) data, cell-level prediction
presents profound challenges. This work explores injecting hierarchical prior
knowledge into graph neural networks (GNNs) for single-cell multi-class
classification of tabular cellular data. By representing the data as graphs and
encoding hierarchical relationships between classes, we propose our
hierarchical plug-in method to be applied to several GNN models, namely,
FCHC-GNN, and effectively designed to capture neighborhood information crucial
for single-cell FC domain. Extensive experiments on our cohort of 19 distinct
patients, demonstrate that incorporating hierarchical biological constraints
boosts performance significantly across multiple metrics compared to baseline
GNNs without such priors. The proposed approach highlights the importance of
structured inductive biases for gaining improved generalization in complex
biological prediction tasks.

该研究通过将数据表示为图表，编码类别之间的层次关系，提出了一种注入层次性先验知识到图神经网络的方法，以应用于单细胞多类别分类的细胞数据，研究表明结构化归纳偏差对于在复杂的生物预测任务中提供改进的泛化能力至关重要。

将层次生物先验注入图神经网络进行流式细胞仪预测

Injecting Hierarchical Biological Priors into Graph Neural Networks for  Flow Cytometry Prediction

Convolutional Neural Network (CNN) image classifiers are traditionally
designed to have sequential convolutional layers with a single output layer.
This is based on the assumption that all target classes should be treated
equally and exclusively. However, some classes can be more difficult to
distinguish than others, and classes may be organized in a hierarchy of
categories. At the same time, a CNN is designed to learn internal
representations that abstract from the input data based on its hierarchical
layered structure. So it is natural to ask if an inverse of this idea can be
applied to learn a model that can predict over a classification hierarchy using
multiple output layers in decreasing order of class abstraction. In this paper,
we introduce a variant of the traditional CNN model named the Branch
Convolutional Neural Network (B-CNN). A B-CNN model outputs multiple
predictions ordered from coarse to fine along the concatenated convolutional
layers corresponding to the hierarchical structure of the target classes, which
can be regarded as a form of prior knowledge on the output. To learn with
B-CNNs a novel training strategy, named the Branch Training strategy
(BT-strategy), is introduced which balances the strictness of the prior with
the freedom to adjust parameters on the output layers to minimize the loss. In
this way we show that CNN based models can be forced to learn successively
coarse to fine concepts in the internal layers at the output stage, and that
hierarchical prior knowledge can be adopted to boost CNN models' classification
performance. Our models are evaluated to show that the B-CNN extensions improve
over the corresponding baseline CNN on the benchmark datasets MNIST, CIFAR-10
and CIFAR-100.

本研究提出了一种变体的卷积神经网络模型 Branch Convolutional Neural Network（B-CNN）和对应的 Branch Training 策略，该模型可以沿着目标类别的层次结构输出多个从粗到细排序的预测结果，利用层次化的先验知识提高卷积神经网络的分类性能。实验结果表明，B-CNN 的性能优于 MNIST、CIFAR-10 和 CIFAR-100 基准数据集上的对应基准 CNN。