Abstract
Accurate prediction of synthetic lethality (SL) is important for guiding the development of cancer drugs and therapies. SL prediction faces significant challenges in the effective fusion of heterogeneous multi-source data. Existing multimodal methods often suffer from "modality laziness" due to disparate convergence speeds, which hinders the exploitation of complementary information. This is also one reason why most existing SL prediction models cannot perform well on both pan-cancer and single-cancer SL pair prediction. In this study, we propose SynLeaF, a dual-stage multimodal fusion framework for SL prediction across pan- and single-cancer contexts. The framework employs a VAE-based cross-encoder with a product of experts mechanism to fuse four omics data types (gene expression, mutation, methylation, and CNV), while simultaneously utilizing a relational graph convolutional network to capture structured gene representations from biomedical knowledge graphs. To mitigate modality laziness, SynLeaF introduces a dual-stage training mechanism employing featurelevel knowledge distillation with adaptive uni-modal teacher and ensemble strategies. In extensive experiments across eight specific cancer types and a pancancer dataset, SynLeaF achieves superior performance in 17 out of 19 scenarios. Ablation studies and gradient analyses further validate the critical contributions of the proposed fusion and distillation mechanisms to model robustness and generalization. To facilitate community use, a web server is available at this https URL.
Abstract (translated)
准确预测合成致死性（SL）对于指导癌症药物和疗法开发具有重要意义。SL预测在有效融合异构多源数据方面面临重大挑战。现有多模态方法常因模态收敛速度差异而出现“模态懒惰性”，这阻碍了互补信息的利用。这也是大多数现有SL预测模型在泛癌和单癌SL对预测上均无法表现良好的原因之一。本研究提出SynLeaF，一个用于泛癌与单癌场景下SL预测的双阶段多模态融合框架。该框架采用基于变分自编码器（VAE）的交叉编码器与专家乘积机制，融合四种组学数据类型（基因表达、突变、甲基化和拷贝数变异），同时利用关系图卷积网络从生物医学知识图中捕获结构化基因表示。为缓解模态懒惰性，SynLeaF引入了采用特征级知识蒸馏的双阶段训练机制，配合自适应单模态教师与集成策略。在涵盖八种特定癌症类型及泛癌数据集的广泛实验中，SynLeaF在19个场景中的17个取得优异性能。消融实验与梯度分析进一步验证了所提出融合与蒸馏机制对模型鲁棒性和泛化能力的关键贡献。为促进社区使用，相关网络服务器已在此https URL 发布。
URL
https://arxiv.org/abs/2603.22369
