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Shan Cong

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3 papers

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JBHI Journal 2026 Journal Article

CAMM: Confidence-Aligned Multiview Multimodal Fusion for Brain Disorders Prediction With Imaging Transcriptomics

Haoran Luo
Zhoujie Fan
Wei Li
Hong Liang
Chen Jason Zhang
Xiaoyong Wei
Zheng Wang
Shan Cong

Brain disorder prediction can be enhanced by models that capture not only imaging phenotypes but also their underlying molecular context. Neuroimaging provides detailed structural and functional information, yet it offers limited insight into the gene-regulated processes driving these alterations. Transcriptomic atlases offer such molecular insights but are rarely available at the subject level due to invasive sampling. To address this gap, we propose CAMM, a confidence-aware multi-modal framework that integrates transcriptomic priors with imaging features to embed molecular context before fusion. CAMM further introduces a unified confidence calibration–regularization strategy that adapts modality contributions at the sample level, ensuring that information from high-confidence samples is leveraged to improve predictions for low-confidence samples, thereby enhancing robustness. Applied to large neuroimaging cohorts, CAMM consistently surpasses state-of-the-art baselines and identifies biologically meaningful biomarkers, demonstrating how transcriptomic priors can bridge molecular mechanisms and imaging for interpretable precision modeling of brain disorders.

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AAAI Conference 2026 Conference Paper

CATAL: Causally Disentangled Task Representation Learning for Offline Meta-Reinforcement Learning

Shan Cong
Chao Yu
Xiangyuan Lan

Context-based Offline Meta Reinforcement Learning (COMRL) has shown promising results in improving the cross-task generalization ability of meta-policies. However, current methods often lead to entangled task representations, in which each latent dimension is influenced by multiple causal factors that govern variations in environment dynamics and reward mechanisms. This entanglement can degrade generalization performance, particularly when multiple causal factors vary simultaneously across tasks. To address this limitation, we propose CAusally disentangled TAsk representation Learning (CATAL) method for COMRL that aims to improve the generalization ability of the meta-policy, where each latent dimension in the task representations aligns to a single causal factor.Theoretically, we show that under mild conditions, the task representations learned by CATAL are causally disentangled. Empirically, extensive results on multi-task MuJoCo benchmarks show that CATAL consistently outperforms existing COMRL baselines in both in-distribution and out-of-distribution generalization.

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JBHI Journal 2024 Journal Article

GREMI: An Explainable Multi-Omics Integration Framework for Enhanced Disease Prediction and Module Identification

Hong Liang
Haoran Luo
Zhiling Sang
Miao Jia
Xiaohan Jiang
Zheng Wang
Shan Cong
Xiaohui Yao

Multi-omics integration has demonstrated promising performance in complex disease prediction. However, existing research typically focuses on maximizing prediction accuracy, while often neglecting the essential task of discovering meaningful biomarkers. This issue is particularly important in biomedicine, as molecules often interact rather than function individually to influence disease outcomes. To this end, we propose a two-phase framework named GREMI to assist multi-omics classification and explanation. In the prediction phase, we propose to improve prediction performance by employing a graph attention architecture on sample-wise co-functional networks to incorporate biomolecular interaction information for enhanced feature representation, followed by the integration of a joint-late mixed strategy and the true-class-probability block to adaptively evaluate classification confidence at both feature and omics levels. In the interpretation phase, we propose a multi-view approach to explain disease outcomes from the interaction module perspective, providing a more intuitive understanding and biomedical rationale. We incorporate Monte Carlo tree search (MCTS) to explore local-view subgraphs and pinpoint modules that highly contribute to disease characterization from the global-view. Extensive experiments demonstrate that the proposed framework outperforms state-of-the-art methods in seven different classification tasks, and our model effectively addresses data mutual interference when the number of omics types increases. We further illustrate the functional- and disease-relevance of the identified modules, as well as validate the classification performance of discovered modules using an independent cohort.

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