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Fei Guo

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19 papers

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AAAI Conference 2026 Conference Paper

Closer to Biological Mechanism: Drug-Drug Interaction Prediction from the Perspective of Pharmacophore

Mingliang Dou
Linfeng Wen
Jinyang Xie
Jijun Tang
Shiqiang Ma
Fei Guo

Drug combinations are widely used in modern medicine but may cause severe adverse drug reactions. Therefore, making effective drug-drug interactions (DDI) prediction is crucial for pharmacovigilance. Existing DDI prediction models are typically built from a structural perspective, assuming that drugs with similar molecular structures may exhibit similar interactions. However, such approaches overlook the biological mechanisms underlying DDI in the human body. This not only weakens the generalization ability of the model, but also makes its interpretability less convincing. Inspired by this, we propose a new method called PC-DDI. Unlike structure-based models, PC-DDI utilizes pharmacophores as basic unit, and designs a complete pharmacophore feature processing framework. It further constructs a pharmacophore-based bipartite graph to model interactions between pharmacophores. This approach allows us to explore the underlying mechanisms of DDI from a functional perspective. We also design a spatial attention weight graph convolution module to optimize the message passing process by integrating pharmacophore position features with node features. Furthermore, we apply causal inference to identify key pharmacophores in pharmacophore bipartite graph, enhancing the interpretability. Compared with the SOTA, PC-DDI achieves an accuracy improvement of 1.84% under the transductive setting and consistently outperforms others in all other experiments.

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AAAI Conference 2026 Conference Paper

Geometry-Aware Variational Information Maximization for Deep Incomplete Multi-view Clustering

Wenlan Chen
Lu Gao
Daoyuan Wang
Fei Guo
Cheng Liang

Incomplete multi-view clustering (IMVC) aims to group data into meaningful clusters when each sample is only partially observed across multiple views. Most existing methods either rely on imputation strategies that may introduce noise and distort the underlying data distribution, or adopt cross-view alignment techniques that focus on pairwise relationships, often resulting in suboptimal representations and unstable clustering performance. In this paper, we propose Geometry-Aware Variational Information Maximization for Deep Incomplete Multi-view Clustering (GAVIM), a novel imputation-free variational framework that enables robust and coherent incomplete multi-view clustering. Specifically, GAVIM leverages mutual information maximization to preserve the high mutual information between the available multi-view data and the shared embedding. Moreover, we explicitly retain local geometric consistency within each view-specific latent space under the guidance of an adaptive global supervision signal. Lastly, GAVIM aligns all views simultaneously using a Gramian representation alignment measure, ensuring coherent structure across modalities and promoting unified, semantically meaningful representations. Extensive experiments on five benchmark IMVC datasets with varying levels of view incompleteness demonstrate that GAVIM consistently outperforms state-of-the-art methods in clustering accuracy and representation quality.

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AAAI Conference 2026 Conference Paper

Make Foundation Models Trustworthy Again: Causal Fine-Adaptation for Medical Image Segmentation

Hongpeng Yang
Yingxin Chen
Shiqiang Ma
Fei Guo

Vision foundation models (e.g., SAM2, CLIP) show strong generalization in natural image analysis but degrade significantly in specialized domains like medical imaging. This is critical for tasks such as brain tumor segmentation, where errors directly affect surgical planning and patient outcomes. In such contexts, segmentation must be highly reliable and structurally precise, underscoring the need for adaptable methods with low error tolerance. While fine-tuning is the dominant strategy, it is computationally expensive and prone to forgetting. To address this, we propose CausalBridgeNet, a causality-guided correction framework for medical image segmentation. Inspired by predictive coding theories of the Bayesian brain, our method introduces a Predictive Causal Reasoning Unit (PCRU) that estimates structured error maps and delivers targeted feedback to iteratively refine predictions. This forms a closed-loop, error-aware correction mechanism without modifying the foundation model. By keeping the backbone frozen, CausalBridgeNet preserves general visual priors while enhancing task-specific accuracy. On the BraTS 2025 benchmark, it achieves an average Dice score of 84.48 and HD95 of 5.48 across tumor subregions, demonstrating its effectiveness for high-precision medical segmentation.

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AAAI Conference 2026 Conference Paper

PharmaQA: Prompt-Based Molecular Representation Learning via Pharmacophore-Oriented Question Answering

Chengwei Ai
Qiaozhen Meng
Mengwei Sun
Ruihan Dong
Hongpeng Yang
Shiqiang Ma
Xiaoyi Liu
Cheng Liang

Molecular representation plays a central role in computational drug discovery. Pharmacophores, functional groups responsible for molecular bioactivity, have been widely studied in cheminformatics. However, their incorporation into molecular representation learning, particularly in a context reasoning or generalization, remains relatively limited. To address this gap, we propose PharmaQA, a pharmacophore oriented question answering framework that formulates tailored prompts to extract context-aware molecular semantics. Rather than encoding pharmacophore features, PharmaQA learns to answer pharmacophore related queries. This design enables flexible reasoning across diverse tasks, including molecular property prediction, compound-target interaction prediction, and binding affinity estimation. Experimental results on benchmark datasets demonstrate that PharmaQA achieves competitive performance. In a ligand discovery case study using FDA-approved compounds, the framework identified potential inhibitors for three therapeutic targets, with strong docking performance. As a generalizable and modular solution, PharmaQA incorporates pharmacophoric knowledge into molecular embeddings, enhancing both predictive accuracy and interpretability in drug discovery applications.

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AAAI Conference 2026 Conference Paper

Piercing the Fog: Disentangling Key Features for Vision Models in Multi-Degradation Scenarios

Siyu Chen
Shiqiang Ma
Fei Guo

In natural scenarios, vision models often encounter the challenge of complex degradation scenarios(e.g., rain, snow, fog, or motion blur). These degradations severely corrupt image features, causing existing models to treat rarely seen or unseen degraded images as “unfamiliar”, thereby losing their inherent recognition and perception capabilities. To address this challenge, we propose a novel degradation disentanglement model (DDM) aimed at precisely disentangling degraded features from the image. The model enhances its perception of various degradations by controlling the matching of features across different degradation types and further strengthens the cross-correlation of target features by introducing a degradation suppression module. This enables the model to re-identify and re-localize targets while removing degradations. We validated the effectiveness of our method on more challenging few-shot segmentation datasets Degraded-Pascal and Degraded-COCO. Results on them outperform SOTA with 3.71% and 3.69% improvement respectively. The experimental results show that our method significantly improves the performance of vision models in various degradation scenarios and provides new ideas and solutions for visual understanding tasks in complex environments.

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JBHI Journal 2025 Journal Article

CellCircLoc: Deep Neural Network for Predicting and Explaining Cell Line-Specific CircRNA Subcellular Localization

Min Zeng
Jingwei Lu
Yiming Li
Chengqian Lu
Shichao Kan
Fei Guo
Min Li

The subcellular localization of circular RNAs (circRNAs) is crucial for understanding their functional relevance and regulatory mechanisms. CircRNA subcellular localization exhibits variations across different cell lines, demonstrating the diversity and complexity of circRNA regulation within distinct cellular contexts. However, existing computational methods for predicting circRNA subcellular localization often ignore the importance of cell line specificity and instead train a general model on aggregated data from all cell lines. Considering the diversity and context-dependent behavior of circRNAs across different cell lines, it is imperative to develop cell line-specific models to accurately predict circRNA subcellular localization. In the study, we proposed CellCircLoc, a sequence-based deep learning model for circRNA subcellular localization prediction, which is trained for different cell lines. CellCircLoc utilizes a combination of convolutional neural networks, Transformer blocks, and bidirectional long short-term memory to capture both sequence local features and long-range dependencies within the sequences. In the Transformer blocks, CellCircLoc uses an attentive convolution mechanism to capture the importance of individual nucleotides. Extensive experiments demonstrate the effectiveness of CellCircLoc in accurately predicting circRNA subcellular localization across different cell lines, outperforming other computational models that do not consider cell line specificity. Moreover, the interpretability of CellCircLoc facilitates the discovery of important motifs associated with circRNA subcellular localization.

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NeurIPS Conference 2025 Conference Paper

Disentangled Cross-Modal Representation Learning with Enhanced Mutual Supervision

Lu Gao
Wenlan Chen
Daoyuan Wang
Fei Guo
Cheng Liang

Cross-modal representation learning aims to extract semantically aligned representations from heterogeneous modalities such as images and text. Existing multimodal VAE-based models often suffer from limited capability to align heterogeneous modalities or lack sufficient structural constraints to clearly separate the modality-specific and shared factors. In this work, we propose a novel framework, termed D isentangled C ross- M odal Representation Learning with E nhanced M utual Supervision (DCMEM). Specifically, our model disentangles the common and distinct information across modalities and regularizes the shared representation learned from each modality in a mutually supervised manner. Moreover, we incorporate the information bottleneck principle into our model to ensure that the shared and modality-specific factors encode exclusive yet complementary information. Notably, our model is designed to be trainable on both complete and partial multimodal datasets with a valid Evidence Lower Bound. Extensive experimental results demonstrate significant improvements of our model over existing methods on various tasks including cross-modal generation, clustering, and classification.

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NeurIPS Conference 2025 Conference Paper

DynaPhArM: Adaptive and Physics-Constrained Modeling for Target-Drug Complexes with Drug-Specific Adaptations

Diya Zhang
Mengwei Sun
Xingdan Wang
Cheng Liang
Qiaozhen Meng
Shiqiang Ma
Fei Guo

Accurately modeling the target-drug complex at atom level presents a significant challenge in the computer-aided drug design. Traditional methods that rely solely on rigid transformations often fail to capture the adaptive interactions between targets and drugs, particularly during substantial conformational changes in targets upon ligand binding, which becomes especially critical when learning target-drug interactions in drug design. Accurately modeling these changes is crucial for understanding target-drug interactions and improving drug efficacy. To address these challenges, we introduce DynaPhArM, an SE(3)-Equivariant Transformer model specifically designed to capture adaptive alterations occurring within target-drug interactions. DynaPhArM utilizes the cooperative scalar-vector representation, drug-specific embeddings, and a diffusion process to effectively model the evolving dynamics of interactions between targets and drugs. Furthermore, we integrate physical information and energetic principles that maintain essential geometric constraints, such as bond lengths, bond angles, van der Waals forces (vdW), within a multi-task learning (MTL) framework to enhance accuracy. Experimental results demonstrate that DynaPhArM achieves state-of-the-art performance with an overall root mean square deviation (RMSD) of 2. 01 Å and a sc-RMSD of 0. 29 Å while exhibiting higher success rates compared to existing methodologies. Additionally, DynaPhArM shows promise in enhancing drug specificity, thereby simulating how targets adapt to various drugs through precise modeling of atomic-level interactions and conformational flexibility.

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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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IJCAI Conference 2025 Conference Paper

Image-Enhanced Hybrid Encoding with Reinforced Contrastive Learning for Spatial Domain Identification in Spatial Transcriptomics

Daoyuan Wang
Lu Gao
Wenlan Chen
Cheng Liang
Fei Guo

Spatial transcriptomics integrates spatial, gene expression, and multichannel immunohistochemistry image data, enabling advanced insights into cellular organization. However, existing methods often struggle to effectively fuse these multimodal data, limiting their potential for accurate spatial domain identification. Here, we propose IE-HERCL (Image-Enhanced Hybrid Encoding with Reinforced Contrastive Learning), a novel framework designed to address this challenge. Specifically, IE-HERCL employs hybrid encoding to capture both the non-spatial features and spatial dependencies for both gene and image modalities via autoencoders and GraphSAGE, respectively. These features are then fused using cross-view attention mechanisms to generate the unified informative embedding. To enhance the representation learning capability, we introduce a reinforced contrastive learning strategy to mitigate the influences of false negative samples, where we detect potential positive counterparts with high-order random walks. In addition, the cluster alignment is dynamically refined through optimal transport, which ensures that the fused consensus representation is coherent and robust, enabling accurate spatial domain identification. Our approach achieves state-of-the-art performance on five image-enhanced spatial transcriptomics datasets, demonstrating its robustness and effectiveness in multimodal integration and spatial domain identification. IE-HERCL offers a powerful and innovative solution for advancing spatial transcriptomics analysis. The code is released on https: //github. com/wdyi701/IE-HERCL.

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EAAI Journal 2025 Journal Article

Incremental self-supervised learning based on transformer for anomaly detection and localization

Wenping Jin
Fei Guo
Qi Wu
Li Zhu

Anomaly detection and localization within image data pose significant challenges in the field of machine learning due to the unsupervised nature of the task, which requires the simultaneous identification of both global and local anomalies without labeled data. To address this challenge, patch-level self-supervised learning methods have traditionally been employed to detect both overall and local anomalies. However, these methods tend to prioritize information from patches, often neglecting critical image details. To overcome this limitation, this paper introduces a novel self-supervised method for anomaly detection and localization. The proposed method leverages the Vision Transformer as its core network and integrates a Vision Transformer-based Masked Autoencoder along with a unique pixel-level self-supervised learning approach. During training, we employ progressive data augmentation techniques, referred to as incremental self-supervised learning. We validate the effectiveness of our method through experiments on real-world industrial anomaly detection datasets, comparing its performance with that of relevant methods. Our code is available at https: //github. com/xjspin/ISSTAD.

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IJCAI Conference 2025 Conference Paper

Unlocking Dark Vision Potential for Medical Image Segmentation

Hongpeng Yang
Xiangyu Hu
Yingxin Chen
Siyu Chen
Srihari Nelakuditi
Yan Tong
Shiqiang Ma
Fei Guo

Accurate segmentation of lesions is crucial for disease diagnosis and treatment planning. However, blurring and low contrast in the imaging process can affect segmentation results. We have observed that noninvasive medical imaging shares considerable similarities with natural images under low light conditions and that nocturnal animals possess extremely strong night vision capabilities. Inspired by the dark vision of these nocturnal animals, we proposed a novel plug-and-play dark vision network (DVNet) to enhance the model's perception for low-contrast medical images. Specifically, by employing the wavelet transform, we decompose medical images into subbands of varying frequencies, mimicking the sensitivity of photoreceptor cells to different light intensities. To simulate the antagonistic receptive fields of horizontal cells and bipolar cells, we design a Mamba-Enhanced Fusion Module to achieve global information correlation and enhance contrast between lesions and surrounding healthy tissues. Extensive experiments demonstrate that the DVNet achieves SOTA performance in various medical image segmentation tasks.

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AAAI Conference 2024 Conference Paper

Forced Exploration in Bandit Problems

Qi Han
Li Zhu
Fei Guo

The multi-armed bandit(MAB) is a classical sequential decision problem. Most work requires assumptions about the reward distribution (e.g., bounded), while practitioners may have difficulty obtaining information about these distributions to design models for their problems, especially in non-stationary MAB problems. This paper aims to design a multi-armed bandit algorithm that can be implemented without using information about the reward distribution while still achieving substantial regret upper bounds. To this end, we propose a novel algorithm alternating between greedy rule and forced exploration. Our method can be applied to Gaussian, Bernoulli and other subgaussian distributions, and its implementation does not require additional information. We employ a unified analysis method for different forced exploration strategies and provide problem-dependent regret upper bounds for stationary and piecewise-stationary settings. Furthermore, we compare our algorithm with popular bandit algorithms on different reward distributions.

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JBHI Journal 2021 Journal Article

iPro2L-PSTKNC: A Two-Layer Predictor for Discovering Various Types of Promoters by Position Specific of Nucleotide Composition

Yinuo Lyu
Wenying He
Shuhao Li
Quan Zou
Fei Guo

Promoters are DNA regulatory elements located proximal to the transcription start site, which are in charge of the initiation of specific gene transcription. In Escherichia coli, promoters can be recognized by σ factors that have multiple families based on distinct function and structure, such as σ 24, σ 28, σ 32, σ 38, σ 54 and σ 70. At present, biological methods are mainly used to identify these promoters. However, because it is time-consuming and material-consuming to do biological experiments, computational biology algorithm has emerged as a more effective way to predict the classification. In this study, we develop a novel two-layer seamless predictor called iPro2L-PSTKNC to identify the promoters of the E. coli genome, which based on the feature extraction model we newly proposed that is named as the position specific tendencies of k-mer nucleotide composition (PSTKNC). On the first layer, it is a binary classification predicting whether a sequence is promoter or not. And the second layer is a multiple classification identifying which type the identified promoter belongs to. The ensemble classification SVM performsbest comparing with other algorithms, which gets a promising accuracy and the Matthews correlation coefficient (MCC) at 90. 05% and 80. 13%. Our data and code are available at https://github.com/lyuyinuo/iPro2L-PSTKNC.

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JBHI Journal 2021 Journal Article

Multi-Scale Time-Series Kernel-Based Learning Method for Brain Disease Diagnosis

Zehua Zhang
Jiaqi Ding
Junhai Xu
Jijun Tang
Fei Guo

The functional magnetic resonance imaging (fMRI) is a noninvasive technique for studying brain activity, such as brain network analysis, neural disease automated diagnosis and so on. However, many existing methods have some drawbacks, such as limitations of graph theory, lack of global topology characteristic, local sensitivity of functional connectivity, and absence of temporal or context information. In addition to many numerical features, fMRI time series data also cover specific contextual knowledge and global fluctuation information. Here, we propose multi-scale time-series kernel-based learning model for brain disease diagnosis, based on Jensen-Shannon divergence. First, we calculate correlation value within and between brain regions over time. In addition, we extract multi-scale synergy expression probability distribution (interactional relation) between brain regions. Also, we produce state transition probability distribution (sequential relation) on single brain regions. Then, we build time-series kernel-based learning model based on Jensen-Shannon divergence to measure similarity of brain functional connectivity. Finally, we provide an efficient system to deal with brain network analysis and neural disease automated diagnosis. On Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, our proposed method achieves accuracy of 0. 8994 and AUC of 0. 8623. On Major Depressive Disorder (MDD) dataset, our proposed method achieves accuracy of 0. 9166 and AUC of 0. 9263. Experiments show that our proposed method outperforms other existing excellent neural disease automated diagnosis approaches. It shows that our novel prediction method performs great accurate for identification of brain diseases as well as existing outstanding prediction tools.

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JBHI Journal 2020 Journal Article

DeepAVP: A Dual-Channel Deep Neural Network for Identifying Variable-Length Antiviral Peptides

Jiawei Li
Yuqian Pu
Jijun Tang
Quan Zou
Fei Guo

Antiviral peptides (AVPs) have been experimentally verified to block virus into host cells, which have antiviral activity with decapeptide amide. Therefore, utilization of experimentally validated antiviral peptides is a potential alternative strategy for targeting medically important viruses. In this article, we propose a dual-channel deep neural network ensemble method for analyzing variable-length antiviral peptides. The LSTM channel can capture long-term dependencies for effectively studying original variable-length sequence data. The CONV channel can build dynamic neural network for analyzing the local evolution information. Also, our model can fine-tune the substitution matrix for specifically functional peptides. Applying it to a novel experimentally verified dataset, our AVPs predictor, DeepAVP, demonstrates state-of-the-art performance of $\text{92. 4}\%$ accuracy and 0. 85 MCC, which is far better than existing prediction methods for identifying antiviral peptides. Therefore, DeepAVP, web server for predicting the effective AVPs, would make significantly contributions to peptide-based antiviral research.

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JBHI Journal 2019 Journal Article

Identification of Drug-Side Effect Association via Semisupervised Model and Multiple Kernel Learning

Yijie Ding
Jijun Tang
Fei Guo

Drug-side effect association contains the information on marketed medicines and their recorded adverse drug reactions. Traditional experimental method is time consuming and expensive. All associations of drugs and side-effects are seen as a bipartite network. Therefore, many computational approaches have been developed to deal with this problem, which are used to predict new potential associations. However, lots of methods did not consider multiple kernel learning (MKL) algorithm, which can integrate multiple sources of information and further improve prediction performance. In this study, we develop a novel predictor of drug-side effect association. First, we build multiple kernels from drug space and side-effect space. What is more, these corresponding kernels are linear weighted by MKL algorithm in drug space and side-effect space, respectively. Finally, a graph-based semisupervised learning is employed to construct drug-side effect predictor. Compared with existing methods, our method achieves better results on three benchmark data sets. The values of area under the precision recall curve are 0. 668, 0. 673, and 0. 670 on three benchmark data sets, respectively. Our method is a useful tool for the side-effects prediction of drugs.

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TCS Journal 2018 Journal Article

Approximation algorithms for the scaffolding problem and its generalizations

Zhi-Zhong Chen
Youta Harada
Fei Guo
Lusheng Wang

Scaffolding is one of the main stages in genome assembly. During this stage, we want to merge contigs assembled from the paired-end reads into bigger chains called scaffolds. For this purpose, the following graph-theoretical problem has been proposed: Given an edge-weighted complete graph G and a perfect matching D of G, we wish to find a Hamiltonian path P in G such that all edges of D appear in P and the total weight of edges in P but not in D is maximized. This problem is NP-hard and the previously best polynomial-time approximation algorithm for it achieves a ratio of 1 2. In this paper, we design a new polynomial-time approximation algorithm achieving a ratio of 5 − 5 ϵ 9 − 8 ϵ for any constant 0 < ϵ < 1. Several generalizations of the problem have also been introduced in the literature and we present polynomial-time approximation algorithms for them that achieve better approximation ratios than the previous bests. In particular, one of the algorithms answers an open question.

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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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