Author name cluster

Ran Su

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10 papers

2 author rows

JBHI Journal 2026 Journal Article

PGST: A prototype-guided parameter-efficient network for spatial transcriptomics prediction

Yuan He
Kaimiao Hu
Changming Sun
Leyi Wei
Ran Su

Spatial transcriptomics (ST) aims to decode spatially resolved gene expression patterns while preserving tissue morphology. Current methods tend to use lower-cost deep learning approaches for gene expression prediction, yet face severe challenges. First, existing methods fail to give sufficient consideration to the spatial specificity of positional encoding inherent in ST; second, they neglect to leverage spatially coherent co-expression patterns across different domains; third, their reliance on linearly weighted aggregation induces vulnerability to noise and distribution shifts; and finally, these architectures exhibit limited parameter efficiency. To address these issues, we introduce prototype-guided network for spatial transcriptomics (PGST), which includes four parts: (1) oriented signal propagation through polar embedding strategy for spatial transcriptomics (PEST); (2) prototype-guided aggregation for global co-feature preservation; (3) global consistency enforcement via shared decoder with reconstruction loss; and (4) lightweight architectural design. Our framework integrates contrastive learning with graph neural networks to balance local-global spatial dependencies and cross-modal consistency. Experimental results on multiple datasets from ST demonstrate the superior performance of our PGST model than existing methods. Our source code is available at: https://github.com/RanSuLab/PGST https://github.com/RanSuLab/PGST.

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JBHI Journal 2025 Journal Article

BFGTP: A BERT-Guided Two-Stage Molecular Representation Learning Framework for Toxicity Prediction

Kaimiao Hu
Yuan He
Jianguo Wei
Changming Sun
Jie Geng
Leyi Wei
Ran Su

Accurate prediction of molecular toxicity is vital for drug development. Most mainstream methods rely on fingerprints or graph-based feature extraction, the emergence of large language models (LLMs) offers new prospects for molecular representation learning in toxicity prediction. Although several studies attempt to leverage LLMs to integrate molecular sequence data for pretraining molecular representations, certain limitations remain. Current LLM-based approaches usually utilize solely on class embedding features, overlooking the rich information in sequence embedding. Moreover, integrating pre-trained molecular representations with multi-modal molecular data may further enhance performance in toxicity prediction. To address these challenges, we propose BFGTP, a BERT-guided two-stage molecular representation learning framework for toxicity prediction. Firstly, we design independent encoders for molecular descriptions of three modalities, where the fingerprint encoder with dual level attention mechanisms effectively integrates multi-category fingerprints. Then, the two-stage guide strategy is introduced to fully utilize the prior knowledge of LLMs, employing contrastive learning to align and fuse the tri-modal representations and knowledge distillation to align predicted value distributions. BFGTP ultimately combines fingerprint and graph representations to predict molecular toxicity. Experiments on seven toxicity datasets show that BFGTP outperforms baselines, achieving the highest AUC on five datasets and the best average performance across five evaluation metrics. Ablation studies, t-SNE visualization and case study confirm the effectiveness of BFGTP's components and its ability to capture meaningful molecular representations.

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JBHI Journal 2025 Journal Article

ERSR: An Ellipse-constrained pseudo-label refinement and symmetric regularization framework for semi-supervised fetal head segmentation in ultrasound images

Linkuan Zhou
Zhexin Chen
Yufei Shen
Junlin Xu
Ping Xuan
Yixin Zhu
Yuqi Fang
Cong Cong

Automated segmentation of the fetal head in ultrasound images is critical for prenatal monitoring. How-ever, achieving robust segmentation remains challenging due to the poor quality of ultrasound images and the lack of annotated data. Semi-supervised methods alleviate the lack of annotated data but struggle with the unique characteristics of fetal head ultrasound images, making it challenging to generate reliable pseudo-labels and enforce effective consistency regularization constraints. To address this issue, we propose a novel semi-supervised framework, ERSR, for fetal head ultrasound segmentation. Our framework consists of the dual-scoring adaptive filtering strategy, the ellipse-constrained pseudo-label refinement, and the symmetry-based multiple consistency regularization. The dual-scoring adaptive filtering strategy uses boundary consistency and contour regularity criteria to evaluate and filter teacher outputs. The ellipse-constrained pseudo-label refinement refines these filtered outputs by fitting leastsquares ellipses, which strengthens pixels near the center of the fitted ellipse and suppresses noise simultaneously. The symmetry-based multiple consistency regularization enforces multi-level consistency across perturbed images, symmetric regions, and between original predictions and pseudo-labels, enabling the model to capture robust and stable shape representations. Our method achieves stateof-the-art performance on two benchmarks. On the HC18 dataset, it reaches Dice scores of 92. 05% and 95. 36% with 10% and 20% labeled data, respectively. On the PSFH dataset, the scores are 91. 68% and 93. 70% under the same settings.

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JBHI Journal 2025 Journal Article

HRProtoKD: A hierarchical and relational prototype based knowledge distillation framework for few-shot cancer molecular subtyping

Fei Guo
Rui Shi
Jia Zhou
Junlin Xu
Hui Cui
Ping Xuan
Xikang Feng
Leyi Wei

Accurate molecular subtyping of cancers is critical for drug discovery and disease treatment but re-mains challenging due to the scarcity of labeled data and the intrinsic heterogeneity of cancer biology. While current methods address this issue via few-shot learning (FSL), they often overlook the hierarchical relation-ships among cancer subtypes and the transfer of relational knowledge between models, both of which are crucial for improving generalization under limited supervision. In this work, we propose HRProtoKD, a novel hierarchical and relational prototype-based knowledge distillation framework designed for few-shot cancer subtype classification. HRProtoKD first employs hierarchical inter-class prototype clustering to capture the underlying class relationships. A prototype-wise contrastive loss is then introduced to enhance intra-class compactness and interclass separability. Furthermore, relational prototype knowledge distillation is applied to transfer structural knowledge from a teacher model to a student model. In addition, we construct three benchmark datasets for few-shot cancer molecular subtyping. Extensive experiments conducted on these datasets demonstrate that HRProtoKD consistently outperforms state-of-the-art meta-learning baselines under both 1-shot and 5-shot learning settings, thereby advancing precision oncology and supporting data-driven approaches for drug discovery and therapeutic development.

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JBHI Journal 2025 Journal Article

Multi-Modal Deep Representation Learning Accurately Identifies and Interprets Drug-Target Interactions

Jiayue Hu
Yuhang Liu
Xiangxiang Zeng
Quan Zou
Ran Su
Leyi Wei

Deep learning offers efficient solutions for drug-target interaction prediction, but current methods often fail to capture the full complexity of multi-modal data (i. e. , sequence, graphs, and three-dimensional structures), limiting both performance and generalization. Here, we present UnitedDTA, a novel explainable deep learning framework capable of integrating multi-modal biomolecule data to improve the binding affinity prediction, especially for novel (unseen) drugs and targets. UnitedDTA enables automatic learning unified discriminative representations from multi-modality data via contrastive learning and cross-attention mechanisms for cross-modality alignment and integration. Comparative results on multiple benchmark datasets show that UnitedDTA significantly outperforms the state-of-the-art drug-target affinity prediction methods and exhibits better generalization ability in predicting unseen drug-target pairs. More importantly, unlike most “black-box” deep learning methods, our well-established model offers better interpretability which enables us to directly infer the important substructures of the drug-target complexes that influence the binding activity, thus providing the insights in unveiling the binding preferences. Moreover, by extending UnitedDTA to other downstream tasks (e. g. , molecular property prediction), we showcase the proposed multi-modal representation learning is capable of capturing the latent molecular representations that are closely associated with the molecular property, demonstrating the broad application potential for advancing the drug discovery process.

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EAAI Journal 2025 Journal Article

PKDF-Net: Anticancer peptide prediction via a prior-knowledge-aware dual-path feature-entangled network

Qiangguo Jin
Ankang Wu
Leyi Wei
Hui Cui
Ping Xuan
Xikang Feng
Ran Su

Anticancer peptides (ACPs) have emerged as promising therapeutic agents due to their ability to selectively target cancer cells. In recent years, various computational methods have been developed to identify ACPs. However, these approaches often struggle to effectively handle the complexity and variability inherent in peptide sequences. To address these challenges, we propose PKDF-Net, a prior-knowledge-aware dual-path feature-entangled network, designed to provide accurate ACP prediction. PKDF-Net consists of three key modules: the prior-knowledge-aware residue interaction representation (PKRI), the Mamba-enhanced amino acid residue representation (MER), and the dual-path feature-entangled prediction (DPFE). The PKRI module integrates prior biological knowledge, the MER module enhances sparse word embeddings within the sequence state space, and the DPFE module entangles dual-path features for precise prediction. Extensive experiments on benchmark datasets demonstrate that PKDF-Net outperforms state-of-the-art models, offering superior performance in ACP prediction. This study presents a promising tool to accelerate ACP development and provides valuable insights for the broader field of biological sequence analysis.

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JBHI Journal 2025 Journal Article

TPNET: A Time-Sensitive Small Sample Multimodal Network for Cardiotoxicity Risk Prediction

Yuan He
Fengyun Zhang
Kaimiao Hu
Changming Sun
Jie Geng
Ning Ren
Ran Su

Cancer therapy-related cardiac dysfunction (CTRCD) is a potential complication associated with cancer treatment, particularly in patients with breast cancer, requiring monitoring of cardiac health during the treatment process. Tissue Doppler imaging (TDI) is a remarkable technique that can provide a comprehensive reflection of the left ventricle's physiological status. We hypothesized that the combination of TDI features with deep learning techniques could be utilized to predict CTRCD. To evaluate the hypothesis, we developed a temporal-multimodal pattern network for efficient training (TPNET) model to predict the incidence of CTRCD over a 24-month period based on TDI, function, and clinical data from 270 patients. Our model achieved an area under curve (AUC) of 0. 83 and sensitivity of 0. 88, demonstrating greater robustness compared to other existing visual models. To further translate our model's findings into practical applications, we utilized the integrated gradients (IG) attribution to perform a detailed evaluation of all the features. This analysis has identified key pathogenic signs that may have remained unnoticed, providing a viable option for implementing our model in preoperative breast cancer patients. Additionally, our findings demonstrate the potential of TPNET in discovering new causative agents for CTRCD.

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AAMAS Conference 2024 Conference Paper

ANOTO: Improving Automated Negotiation via Offline-to-Online Reinforcement Learning

Siqi Chen
Jianing Zhao
Kai Zhao
Gerhard Weiss
Fengyun Zhang
Ran Su
Yang Dong
Daqian Li

Automated negotiation is a crucial component for establishing cooperation and collaboration within multi-agent systems. While reinforcement learning (RL)-based negotiating agents have achieved remarkable success in various scenarios, they still face limitations due to certain assumptions on which they are based. In this work, we proposes a novel approach called ANOTO to improve the negotiating agents’ ability via offline-to-online RL. ANOTO enables a negotiating agent (1) to communicate with opponents using an end-to-end strategy that covers all negotiation actions, (2) to learn negotiation strategies from historical offline data without requiring active interactions, and (3) to enhance the optimization process during the online phase, facilitating rapid and stable performance improvements for the learned offline strategies. Experimental results, based on a number of negotiation scenarios and recent winning agents from the Automated Negotiating Agents Competitions (ANAC), are provided.

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UAI Conference 2023 Conference Paper

An effective negotiating agent framework based on deep offline reinforcement learning

Siqi Chen 0001
Jianing Zhao
Gerhard Weiß 0001
Ran Su
Kaiyou Lei

Learning is crucial for automated negotiation, and recent years have witnessed a remarkable achievement in application of reinforcement learning (RL) for various negotiation tasks. Conventional RL methods focus generally on learning from active interactions with opposing negotiators. However, collecting online data is expensive in many realistic negotiation scenarios. While previous studies partially mitigate this problem through the use of opponent simulators (i. e. , agents following known strategies), in reality it is usually hard to fully capture an opponent’s negotiation strategy. Moreover, a further challenge lies in an agent’s capability of adapting to dynamic variations of an opponent’s preferences or strategies, which may happen from time to time for different reasons in subsequent negotiations. In response to these challenges, this article proposes a novel Deep Offline Reinforcement learning Negotiating Agent framework that allows to learn an effective strategy using previously collected negotiation datasets without requiring interaction with an opponent. This is in contrast to existing RL-based negotiation approaches that all rely on active interaction with opponents. Furthermore, the strategy fine-tuning mechanism is included to adjust the learned strategy in response to the preferences or strategy changes of the opponent. The performance of the proposed framework is evaluated based on a diverse set of state-of-the-art baselines under different settings. Experimental results show that the framework allows to learn effective strategies exclusively with offline datasets, and is also capable of effectively adapting to changes of an opponent’s preferences or strategy.

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AIIM Journal 2017 Journal Article

Improved prediction of protein–protein interactions using novel negative samples, features, and an ensemble classifier

Leyi Wei
Pengwei Xing
Jiancang Zeng
JinXiu Chen
Ran Su
Fei Guo

Computational methods are employed in bioinformatics to predict protein–protein interactions (PPIs). PPIs and protein–protein non-interactions (PPNIs) display different levels of development, and the number of PPIs is considerably greater than that of PPNIs. This significant difference in the number of PPIs and PPNIs increases the cost of constructing a balanced dataset. PPIs can be classified as either physical or genetic. However, ready-made PPNI databases were proven only to have no physical interactions and were not proven to have no genetic interactions. Hence, ready-made PPNI databases contain false negative non-interactions. In this study, two PPNI datasets were artificially generated from a PPI database. In contrast to various traditional PPI feature extraction methods based on sequential information, two types of novel feature extraction methods were proposed. One is based on secondary structure information, and the other is based on the physicochemical properties of proteins. The experimental results of the RandomPairs dataset validate the efficiency and effectiveness of the proposed prediction model. These results reveal the potential of constructing a PPI negative dataset to reduce false negatives. Related datasets, tools, and source codes are accessible at http: //lab. malab. cn/soft/PPIPre/PPIPre. html.

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