MOTIVATION
Identifying transcriptomic factors with potential causal effects on breast cancer progression is important for understanding disease mechanisms and prioritizing therapeutic targets. However, causal-effect estimation from high-dimensional gene-expression data remains challenging because of the large number of variables and potential unmeasured confounding.
RESULTS
We propose CICIV, a causal inference framework that integrates PC-simple-based causal feature selection with conditional instrumental variable (CIV) representation learning. PC-simple first reduces the dimensionality of transcriptomic data by identifying candidate parent genes, after which CIV estimates and ranks their absolute causal effects. Applied to TCGA-BRCA, CICIV prioritized 40 breast cancer-related candidate genes and identified signals that were not captured by conventional correlation-based analyses. External validation using the independent METABRIC cohort showed consistent effect directions for 21 of the 40 genes, with four genes overlapping in the Top 10 and nine in the Top 20 rankings. Pathway enrichment and literature-based analyses further supported the biological relevance of the prioritized genes.
AVAILABILITY AND IMPLEMENTATION
The CICIV benchmarking framework and source code are freely available at https://github.com/Zaiwen/CICIV. The software version and test data used in this study are archived at Zenodo (DOI: 10.5281/zenodo.22143976).
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
Learning rate (LR) initialization and decay remain important factors in the optimization of deep vision networks. Although these models exhibit a clear hierarchical structure, training typically starts from a single global learning rate, with little explicit consideration of stage depth. This paper investigates a simple depth-aware alternative. We propose Asynchronous Learning Rate (ALR), which assigns depth-dependent initial learning rates to network modules according to their topological depth, and Smoothed Synchronous Decay (SSD), which coordinates the subsequent decay of heterogeneous parameter groups. A linear depth rule is adopted as a low-complexity, monotonic parameterization that is straightforward to implement across hierarchical architectures. The proposed strategy is evaluated on image classification and semantic segmentation benchmarks, including CIFAR-100, Mini-ImageNet, ImageNet-1K, Pascal VOC 2012, and LiTS. Additional ResNet-50 evaluations under benchmark-specific training protocols show that depth-scaled ALR improves Top-1 accuracy from 77.618% to 78.736% on native CIFAR-100 across five seeds and from 76.33% to 78.58% on ImageNet-1K. The results indicate that ALR generally outperforms uniform learning rate initialization, whereas SSD is most effective when combined with depth-aware initialization, particularly when layer-wise learning rates would otherwise follow misaligned decay trajectories. These findings suggest that a simple hierarchy-aware learning rate design can serve as an effective optimization refinement for hierarchical vision models.
Qiang He, Qiu Zong, Yi-Qi Wang et al.· IEEE Access· 0 citations
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