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Wang Li

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5 papers

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JBHI Journal 2026 Journal Article

Diff-DTI: Fast Diffusion Tensor Imaging Using A Feature-Enhanced Joint Diffusion Model

Lang Zhang
Jinling He
Wang Li
Dong Liang
Yanjie Zhu

Magnetic resonance diffusion tensor imaging (DTI) is a unique non-invasive technique for measuring in vivo water molecule diffusion, reflecting tissue microstructure. However, acquiring high-quality DTI typically requires numerous diffusion-weighted images (DWIs) in multiple directions, resulting in long scan times that restrict its use in clinical and research settings. To address this limitation, we propose Diff-DTI, a fast DTI processing framework based on a feature-enhanced joint diffusion model, to reduce the number of DWIs needed for tensor fitting. Diff-DTI models the joint probability distribution of DWIs and DTI maps, supporting guided generation during inference. The incorporated feature enhancement fusion module further enhances image precision and details generated by the diffusion model. Experiments were performed on three public DWI datasets. Results demonstrate that Diff-DTI achieves up to 10-fold acceleration (using 6 DWIs) while maintaining relatively low normalized mean square error (NMSE) for DTI maps (2. 89% for FA, 0. 89% for MD, 0. 95% for AD, and 0. 98% for RD). Even using Diff-DTI with only 3 DWIs, the NMSEs of the generated DTI maps showed a gradual decrease, with 3. 51% for FA, 0. 89% for MD, 1. 13% for AD, and 1. 10% for RD. We conclude that Diff-DTI can significantly reduce the number of acquired DWIs and the scan time, without compromising image quality too much.

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EAAI Journal 2026 Journal Article

Explainable multiscale representation learning for anticancer peptide prediction

Yongqing Zhang
Xinyu Mao
Yuhang Liu
Wang Li
Zhigan Zhou
Yugui Xu
Jin Wu
Quan Zou

Anticancer peptides (ACPs) exhibit significant pharmacological potential in studying cancer and other diseases. Although anticancer peptides have been extensively investigated, many remain undiscovered and hold promise as future therapeutics. Computational methods offer efficient solutions for identifying novel anticancer peptides, but current approaches often rely heavily on local amino acid sequence information, overlooking global interactions, which limits their performance and generalizability. Here, we present an explainable multiscale representation learning framework that integrates sequence information from bidirectional encoder and handcrafted features to enhance anticancer peptides predictions. Our model learns sequence representations at two scales: it captures local representations through handcrafted feature methods at residue scale and global representations through a bidirectional encoder at protein scale. In addition, it employs a cross-attention mechanism to fuse these two types of representations automatically. Compared to state-of-the-art techniques, our model demonstrates superior performance with a significant improvement of 2. 3%, 1. 3%, 2. 5% and 3. 2% in prediction accuracy. To further demonstrate the superiority of our model, it can achieve optimal performance even under conditions where negative samples are randomly generated in the alternate dataset and other datasets, resulting in a significant improvement in accuracy over current state-of-the-art techniques. Moreover, model provides “white-box” prediction, revealing representation shifts during inference and effectively identifying important subregions of sequences, thereby uncovering sequence motifs. Shapley additive explanation value analysis was performed on the fused features, revealing that certain features contribute significantly to the model’s predictions. In general, our model is a powerful tool for advancing artificial intelligence-driven drug discovery.

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NeurIPS Conference 2025 Conference Paper

ObCLIP: Oblivious CLoud-Device Hybrid Image Generation with Privacy Preservation

Haoqi Wu
Wei Dai
Ming Xu
Wang Li
Qiang Yan

Diffusion Models have gained significant popularity due to their remarkable capabilities in image generation, albeit at the cost of intensive computation requirement. Meanwhile, despite their widespread deployment in inference services such as Midjourney, concerns about the potential leakage of sensitive information in uploaded user prompts have arisen. Existing solutions either fail to strike an effective balance between utility and efficiency, or lack rigorous privacy guarantees. To bridge this gap, we propose ObCLIP, a plug-and-play safeguard that enables oblivious cloud-device hybrid generation scheme. By oblivious, each input prompt is transformed into a set of semantically similar candidate prompts that differ only in sensitive attributes (e. g. , gender, ethnicity). The cloud server processes all candidate prompts without knowing which one is the real one, thus preventing any prompt leakage. To mitigate server cost, only a small portion of denoising steps is performed upon the large cloud model. The resulting intermediate latents are then transmitted back to the device, which selects the targeted latent and completes the remaining denoising using a small local model to obtain the final image. Additionally, we analyze and incorporate several cache-based accelerations that leverage temporal and batch redundancy, effectively reducing computation cost with minimal utility degradation. Extensive experiments across multiple datasets demonstrate that ObCLIP provides rigorous privacy and comparable utility to large cloud models with slightly increased server computation.

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JBHI Journal 2024 Journal Article

A Transferability-Based Method for Evaluating the Protein Representation Learning

Fan Hu
Weihong Zhang
Huazhen Huang
Wang Li
Yang Li
Peng Yin

Self-supervised pre-trained language models have recently risen as a powerful approach in learning protein representations, showing exceptional effectiveness in various biological tasks, such as drug discovery. Amidst the evolving trend in protein language model development, there is an observable shift towards employing large-scale multimodal and multitask models. However, the predominant reliance on empirical assessments using specific benchmark datasets for evaluating these models raises concerns about the comprehensiveness and efficiency of current evaluation methods. Addressing this gap, our study introduces a novel quantitative approach for estimating the performance of transferring multi-task pre-trained protein representations to downstream tasks. This transferability-based method is designed to quantify the similarities in latent space distributions between pre-trained features and those fine-tuned for downstream tasks. It encompasses a broad spectrum, covering multiple domains and a variety of heterogeneous tasks. To validate this method, we constructed a diverse set of protein-specific pre-training tasks. The resulting protein representations were then evaluated across several downstream biological tasks. Our experimental results demonstrate a robust correlation between the transferability scores obtained using our method and the actual transfer performance observed. This significant correlation highlights the potential of our method as a more comprehensive and efficient tool for evaluating protein representation learning.

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NeurIPS Conference 2021 Conference Paper

Exploiting Data Sparsity in Secure Cross-Platform Social Recommendation

Jinming Cui
Chaochao Chen
Lingjuan Lyu
Carl Yang
Wang Li

Social recommendation has shown promising improvements over traditional systems since it leverages social correlation data as an additional input. Most existing work assumes that all data are available to the recommendation platform. However, in practice, user-item interaction data (e. g. ,rating) and user-user social data are usually generated by different platforms, and both of which contain sensitive information. Therefore, "How to perform secure and efficient social recommendation across different platforms, where the data are highly-sparse in nature" remains an important challenge. In this work, we bring secure computation techniques into social recommendation, and propose S3Rec, a sparsity-aware secure cross-platform social recommendation framework. As a result, our model can not only improve the recommendation performance of the rating platform by incorporating the sparse social data on the social platform, but also protect data privacy of both platforms. Moreover, to further improve model training efficiency, we propose two secure sparse matrix multiplication protocols based on homomorphic encryption and private information retrieval. Our experiments on two benchmark datasets demonstrate the effectiveness of S3Rec.

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