PO.BCS01.10 · 生物信息与计算
DAG引导的核学习框架以缓解乳腺癌中的种族差异
DAG-guided kernel learning framework to mitigate racial disparities in breast cancer
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
作为美国女性癌症死亡的第二大原因，乳腺癌（BC）死亡率在黑人女性中不成比例地高于非西班牙裔白人女性。人工智能（AI）和机器学习（ML）方法已成功应用于缓解BC中的种族差异。例如，我们此前开发了一个多模态迁移学习（TL）框架，可通过将在多数群体（如非西班牙裔白人女性）上学到的知识迁移到少数群体（如黑人女性），以改善AI/ML模型的性能，从而帮助解决健康差异。然而，这些方法以简单的方式整合不同的组学数据，未能捕捉它们之间复杂的跨组学交互，导致在减少BC健康差异方面仅有微小或适度的性能改善。为解决这些问题，我们提出了一个有向无环图（DAG）引导的核学习框架以缓解乳腺癌中的种族差异。具体而言，我们首先通过一种称为NOTEARS（通过迹指数和增广拉格朗日进行结构学习的非组合优化）的方法，将跨组学交互知识编码到DAG图中。然后，我们对学到的图进行硬掩蔽，仅保留与生物学一致的投影。从这些投影中，我们计算特定于投影的RBF核以捕捉非线性样本相似性，产生可解释、方向感知的核，用于训练配备数据增强（DA）方法的TL模型，以减少乳腺癌差异。通过使用来自The Cancer Genome Atlas（TCGA）BRCA队列中1,085名女性BC患者的三种组学模态（即mRNA、miRNA和DNA甲基化），我们证明，在特定时间的无进展间期（PFI）预测方面，与最先进的方法相比，我们提出的框架在减少BC健康差异上取得了显著更优的性能（采用ROC-AUC、PR-AUC、准确率和F1分数等多种性能指标）。此外，我们的框架能够量化并识别多个关键的跨模态交互以缓解BC种族差异。总之，我们提出的框架为探索跨组学交互提供了一种新方法，以提升稳健的TL模型性能，从而减少BC中的健康差异。我们还相信，我们的方法可以扩展到缓解其他类型癌症的种族差异。
查看英文原文 English abstract
As the second leading cause of cancer death among women in the United States, breast cancer (BC) mortality is disproportionately higher among Black women than among non-Hispanic White women. Artificial intelligence (AI) and machine learning (ML) approaches have been successfully applied to mitigate racial disparities in BC. For instance, previously we developed a multi-modal transfer learning (TL) framework that can help address health disparities by transfer knowledge learned on the majority group (e.g., non-Hispanic White women) to improve performance of AI/ML models for a minority group (e.g., Black women). However, these approaches integrate different omics data in a simplistic manner, but failed to capture the complex cross-omics interaction among them, leading to minor or modest performance improvement for reducing BC health disparities. To address these concerns, we propose a directed acyclic graph (DAG)-guided kernel learning framework to mitigate racial disparities in breast cancer. Specifically, we first encoded the cross-omics interaction knowledge into a DAG graph by a method called non-combinatorial optimization via trace exponential and augmented Lagrangian for structure learning (NOTEARS). Then, we hard-masked the learned graph to retain only biology-consistent projections. From these projections, we computed projection-specific RBF kernels to capture non-linear sample similarity, yielding interpretable, direction-aware kernels used for training a TL model equipped with a data augmentation (DA) method, which were used to reduce breast cancer disparities. By using three omics modalities (i.e., mRNA, miRNA, and DNA methylation) from The Cancer Genome Atlas (TCGA) BRCA cohorts of 1,085 female BC patients, we demonstrated that our proposed framework achieves remarkably better performance (using various performance metrics like ROC-AUC, PR-AUC, Accuracy, and F1-score) compared with state-of-the-art approaches for reducing BC health disparities in terms of time-specific Progression-Free Interval (PFI) predictions. In addition, our framework could quantify and identify multiple key cross-modality interactions to mitigate BC racial disparities. In summary, our proposed framework would provide a novel approach to explore cross-omics interactions for boosting a robust TL model performance to reduce health disparities in BC. We also believe our approach can be extended to mitigate racial disparities for other types of cancer.
利益披露 Disclosure
M. Baek, None..
J. Wang, None..
V. Band, None..
S. Wan, None.