For predicting cancer survival outcomes, standard approaches in clinical
research are often based on two main modalities: pathology images for observing
cell morphology features, and genomic (e.g., bulk RNA-seq) for quantifying gene
expressions. However, existing pathology-genomic multi-modal algorithms face
significant challenges: (1) Valuable biological insights regarding genes and
gene-gene interactions are frequently overlooked; (2) one modality often
dominates the optimization process, causing inadequate training for the other
modality. In this paper, we introduce a new multi-modal ``Path-GPTOmic"
framework for cancer survival outcome prediction. First, to extract valuable
biological insights, we regulate the embedding space of a foundation model,
scGPT, initially trained on single-cell RNA-seq data, making it adaptable for
bulk RNA-seq data. Second, to address the imbalance-between-modalities problem,
we propose a gradient modulation mechanism tailored to the Cox partial
likelihood loss for survival prediction. The contributions of the modalities
are dynamically monitored and adjusted during the training process, encouraging
that both modalities are sufficiently trained. Evaluated on two TCGA(The Cancer
Genome Atlas) datasets, our model achieves substantially improved survival
prediction accuracy.

通过多模态框架和生物学洞察来提高癌症生存预测准确性。

Path-GPTOmic: 一个平衡的多模态学习框架用于生存结局预测

Path-GPTOmic: A Balanced Multi-modal Learning Framework for Survival  Outcome Prediction

Single-cell RNA sequencing (scRNA-seq) technology provides high-throughput
gene expression data to study the cellular heterogeneity and dynamics of
complex organisms. Graph neural networks (GNNs) have been widely used for
automatic cell type classification, which is a fundamental problem to solve in
scRNA-seq analysis. However, existing methods do not sufficiently exploit both
gene-gene and cell-cell relationships, and thus the true potential of GNNs is
not realized. In this work, we propose a bilevel graph representation learning
method, named scBiGNN, to simultaneously mine the relationships at both gene
and cell levels for more accurate single-cell classification. Specifically,
scBiGNN comprises two GNN modules to identify cell types. A gene-level GNN is
established to adaptively learn gene-gene interactions and cell representations
via the self-attention mechanism, and a cell-level GNN builds on the cell-cell
graph that is constructed from the cell representations generated by the
gene-level GNN. To tackle the scalability issue for processing a large number
of cells, scBiGNN adopts an Expectation Maximization (EM) framework in which
the two modules are alternately trained via the E-step and M-step to learn from
each other. Through this interaction, the gene- and cell-level structural
information is integrated to gradually enhance the classification performance
of both GNN modules. Experiments on benchmark datasets demonstrate that our
scBiGNN outperforms a variety of existing methods for cell type classification
from scRNA-seq data.

通过建立基于基因和细胞层面的双层图表征学习方法（scBiGNN），在单细胞分类中同时挖掘基因之间和细胞之间的关系，以提高分类准确性。实验证明，scBiGNN 在单细胞 RNA 测序数据的细胞类型分类中优于现有方法。