Predicting patient features from single-cell data can unveil cellular states
implicated in health and disease. Linear models and average cell type
expressions are typically favored for this task for their efficiency and
robustness, but they overlook the rich cell heterogeneity inherent in
single-cell data. To address this gap, we introduce GMIL, a framework
integrating Generalized Linear Mixed Models (GLMM) and Multiple Instance
Learning (MIL), upholding the advantages of linear models while modeling
cell-state heterogeneity. By leveraging predefined cell embeddings, GMIL
enhances computational efficiency and aligns with recent advancements in
single-cell representation learning. Our empirical results reveal that GMIL
outperforms existing MIL models in single-cell datasets, uncovering new
associations and elucidating biological mechanisms across different domains.

GMIL 是一种整合广义线性混合模型（GLMM）和多示例学习（MIL）的框架，利用预定义的细胞嵌入增强计算效率，揭示了单细胞数据中的新关联和揭示了不同领域的生物学机制。

基于注意力的多实例混合模型

Attention-based Multi-instance Mixed Models

The diffusion of rumors on microblogs generally follows a propagation tree
structure, that provides valuable clues on how an original message is
transmitted and responded by users over time. Recent studies reveal that rumor
detection and stance detection are two different but relevant tasks which can
jointly enhance each other, e.g., rumors can be debunked by cross-checking the
stances conveyed by their relevant microblog posts, and stances are also
conditioned on the nature of the rumor. However, most stance detection methods
require enormous post-level stance labels for training, which are
labor-intensive given a large number of posts. Enlightened by Multiple Instance
Learning (MIL) scheme, we first represent the diffusion of claims with
bottom-up and top-down trees, then propose two tree-structured weakly
supervised frameworks to jointly classify rumors and stances, where only the
bag-level labels concerning claim's veracity are needed. Specifically, we
convert the multi-class problem into a multiple MIL-based binary classification
problem where each binary model focuses on differentiating a target stance or
rumor type and other types. Finally, we propose a hierarchical attention
mechanism to aggregate the binary predictions, including (1) a bottom-up or
top-down tree attention layer to aggregate binary stances into binary veracity;
and (2) a discriminative attention layer to aggregate the binary class into
finer-grained classes. Extensive experiments conducted on three Twitter-based
datasets demonstrate promising performance of our model on both claim-level
rumor detection and post-level stance classification compared with
state-of-the-art methods.

通过多示例学习方案，我们提出了基于树结构的弱监督框架来联合分类谣言和立场，这里只需要关于真实性的包级标签。最后，我们提出了分层注意力机制来聚合二进制预测，通过在三个 Twitter 数据集上进行广泛的实验，证明了我们的模型在与现有方法相比的认领级别的谣言检测和帖子级别的立场分类方面具有良好的性能。