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Lan Huang

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14 papers

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JBHI Journal 2026 Journal Article

A Dynamic Multi-Scale Hypergraph Learning Framework Driven by Features and Structures for ceRNA-Disease Association Prediction

Xin-Fei Wang
Lan Huang
Yan Wang
Ren-Chu Guan
Zhu-Hong You
Feng-Feng Zhou
Yu-Qing Li
Yuan Fu

Competitive endogenous RNA (ceRNA) networks are pivotal for uncovering disease molecular mechanisms. Graph representation learning is a cornerstone for modeling biological regulatory networks and predicting disease-related biomarkers. However, current methods face challenges: traditional graph neural network (GNN) rely on low-order graph structures, which struggle to capture high-order molecular interactions, resulting in topological information loss; shallow GNN fail to model long-range dependencies, while deep architectures suffer from over-smoothing, limiting complex regulatory expression; static embeddings overlook dynamic molecular interactions, reducing biomarker accuracy. These limitations highlight the need for advanced graph learning frameworks. To address these challenges, we propose DMHLF, a Dynamic Multi-scale Hypergraph Learning Framework for predicting disease-associated ceRNA biomarkers. The framework first integrates multiple regulatory relationships among miRNAs, lncRNAs, circRNAs, mRNAs, and diseases to construct disease-specific ceRNA regulatory networks, capturing local and global regulatory patterns through multi-Hop hyperedges. Subsequently, we devise a Hypergraph-Weighted Dynamic Random Walk (HEDRW) method to dynamically extract node meta-embeddings that encode high-order regulatory information. Concurrently, we extend Eigen-GNN spectral analysis to hypergraph structures, incorporating a residual-enhanced hypergraph neural network to preserve the global topological properties of shallow hypergraphs. Finally, a cross-scale attention mechanism aligns and fuses multi-scale features to generate high-quality node embeddings for disease-ceRNA association prediction. Experiments on diverse datasets demonstrate that DMHLF significantly outperforms existing methods. Case study further validates the framework’s efficacy in identifying disease-related ceRNA biomarkers, providing a reliable predictive tool for biomedical research.

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JBHI Journal 2026 Journal Article

Consensus and Complementary Feature Guided Multi-modal Knowledge Distillation Network for Breast Cancer Diagnosis

Shuyu Guo
Lan Huang
Ke Tao
Ruihong Zhao
Tian Bai

The integration of Hematoxylin and Eosin (H&E) and Immunohistochemistry (IHC) staining offers complementary information for accurate assessment of HER2 status in breast cancer. While multi-modal approaches enhance diagnostic precision, the high cost and acquisition constraints of IHC limit their routine clinical application. To address this, we propose a two-stage diagnostic framework that enables effective HER2 grading using only H&E images. In the first stage, a Consensus and Complementary Feature Co-Embedding Network (CoCoFNet) extracts modality-specific and cross-modal features to fully exploit multi-modal representations. In the second stage, a Hierarchical Multi-modal Knowledge Distillation (HM-KD) strategy transfers discriminative knowledge from the multi-modal teacher to a unimodal student network. Experiments on two public datasets demonstrate that the proposed method achieves comparable performance with state-of-the-art distillation methods using only H&E for HER2 grading. Furthermore, CoCoFNet demonstrates superior fusion capability, leading to more effective supervision and improved generalization in the unimodal student network. The codes can be publicly available from https://github.com/syguo95/HMKD.

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JBHI Journal 2026 Journal Article

Reliable Multimodal Cancer Survival Prediction With Confidence-Aware Risk Modeling

Xuping Xie
Ye Wang
Ziqi Zhao
Qixing Yang
Lan Huang
Fengfeng Zhou
Yan Wang

Multimodal survival methods that integrate histology whole-slide images and transcriptomic profiles hold significant promise for understanding patient prognostication and guiding personalized treatment strategies. However, existing approaches primarily focus on improving predictive performance through multimodal information fusion, often neglecting the reliability estimation of the prediction results and the inherent alignment noise across modalities. Thus, we propose ReCaSP, a novel and reliable cancer survival prediction framework that effectively integrates histology and transcriptomics data via multimodal alignment and fusion, providing the auxiliary confidence levels for survival predictions through a confidence-aware risk modeling mechanism. Specifically, our approach incorporates a fine-grained risk classifier that models risk labels jointly over both multiple time intervals and censorship status, utilizing evidential deep learning to yield fine-grained risk predictions accompanied by confidence scores. Additionally, to mitigate the inherent noise in multimodal data alignment, we introduce a cross-attention alignment module that effectively aligns histology data with transcriptomics data prior to multimodal fusion, thereby facilitating cross-modal interaction learning. Extensive experiments on five datasets demonstrate that ReCaSP significantly outperforms state-of-the-art methods, achieving a 4. 58% improvement in the overall C-Index.

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JBHI Journal 2026 Journal Article

Structure and Semantics Aware Multi-View Contrastive Learning for predicting association among lncRNAs, miRNAs and diseases

Lan Huang
Yujuan Zhang
Chenghao Li
Yuan Fu
Yan Wang
Nan Sheng

Exploring associations among long non coding RNAs (lncRNAs), microRNAs (miRNAs), and dis eases is crucial for biomarker discovery and precision medicine. Existing computational methods are hindered by sparse known associations and the complexity of bi ological networks. To address this challenge, we pro pose SSMVCL (Structure- and Semantic-aware Multi-View Contrastive Learning), a unified framework for predicting lncRNA-disease associations (LDAs), miRNA-disease associations (MDAs), and lncRNA-miRNA interactions (LMIs). SSMVCL constructs a heterogeneous bioinformatics network from multi-source biological data and learns representations from two complementary views: a structure-aware view for local topology and a semantic-aware view using biologically meaningful meta-paths to capture high-order relationships. A cross-view contrastive alignment module with adaptive negative sampling enforces consistency between views and enhances discriminative capability. On two benchmark datasets, SSMVCL achieves state-of-the art performance: for Dataset2, AUC/AUPR of 0. 9736/0. 9716 (LDA), 0. 9364/0. 9309 (MDA), and 0. 9297/0. 9234 (LMI) Case studies on gastric and prostate cancers further validated robustness and translational potential by identifying supported associations.

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AAAI Conference 2025 Conference Paper

A Simple Graph Contrastive Learning Framework for Short Text Classification

Yonghao Liu
Fausto Giunchiglia
Lan Huang
Ximing Li
Xiaoyue Feng
Renchu Guan

Short text classification has gained significant attention in the information age due to its prevalence and real-world applications. Recent advancements in graph learning combined with contrastive learning have shown promising results in addressing the challenges of semantic sparsity and limited labeled data in short text classification. However, existing models have certain limitations. They rely on explicit data augmentation techniques to generate contrastive views, resulting in semantic corruption and noise. Additionally, these models only focus on learning the intrinsic consistency between the generated views, neglecting valuable discriminative information from other potential views. To address these issues, we propose a Simple graph contrastive learning framework for Short Text Classification (SimSTC). Our approach involves performing graph learning on multiple text-related component graphs to obtain multi-view text embeddings. Subsequently, we directly apply contrastive learning on these embeddings. Notably, our method eliminates the need for data augmentation operations to generate contrastive views while still leveraging the benefits of multi-view contrastive learning. Despite its simplicity, our model achieves outstanding performance, surpassing large language models on various datasets.

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AAAI Conference 2025 Conference Paper

Boosting Short Text Classification with Multi-Source Information Exploration and Dual-Level Contrastive Learning

Yonghao Liu
Mengyu Li
Wei Pang
Fausto Giunchiglia
Lan Huang
Xiaoyue Feng
Renchu Guan

Short text classification, as a research subtopic in natural language processing, is more challenging due to its semantic sparsity and insufficient labeled samples in practical scenarios. We propose a novel model named MI-DELIGHT for short text classification in this work. Specifically, it first performs multi-source information (i.e., statistical information, linguistic information, and factual information) exploration to alleviate the sparsity issues. Then, the graph learning approach is adopted to learn the representation of short texts, which are presented in graph forms. Moreover, we introduce a dual-level (i.e., instance-level and cluster-level) contrastive learning auxiliary task to effectively capture different-grained contrastive information within massive unlabeled data. Meanwhile, previous models merely perform the main task and auxiliary tasks in parallel, without considering the relationship among tasks. Therefore, we introduce a hierarchical architecture to explicitly model the correlations between tasks. We conduct extensive experiments across various benchmark datasets, demonstrating that MI-DELIGHT significantly surpasses previous competitive models. It even outperforms popular large language models on several datasets.

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EAAI Journal 2025 Journal Article

Reconstructing three-dimensional conductivity distribution of in-situ maize ears using frequency-enhanced residual encoder-decoder network

Hai-Ying Zheng
Bing-Zhou Chen
Yang Xiang
Ke-Lei Xia
Yu-Bin Lin
Jin-Hang Liu
Jia-Jia Zhang
Liu-Deng Zhang

Monitoring water content distribution in in-situ maize ears is crucial for agricultural cultivation and crop research, with impedance dynamics correlated to water content changes. However, the combined effects of complex maize ear structure, unstable contact impedance, and diverse environmental noise significantly reduce the quality of measurement signals, posing major challenges for reconstructing three-dimensional (3D) conductivity distribution. Therefore, we propose a 3D conductivity absolute reconstruction model based on a spectrum-enhanced residual encoder-decoder (FRED-Net), enabling stable monitoring of impedance changes in in-situ maize ears in a greenhouse. FRED-Net combines residual blocks and encoder-decoder architectures, utilizing skip connections to preserve low-level features, effectively addressing gradient vanishing and information loss while maintaining low computational complexity. To cope with greenhouse environmental interference, FRED-Net incorporates an information fusion method based on spectral characteristics and volatility features, combined with a noiseless dataset training mode to enrich model input variability. Results from standard water tank simulation experiments indicate that FRED-Net exhibits ideal reconstruction accuracy and noise robustness compared to existing 3D absolute reconstruction algorithms, with a structural similarity index of 0. 9970, root mean square error of 0. 0019, relative error of 0. 0199, and coefficient of determination of 0. 9610. Imaging experiments on in-situ maize ears in the greenhouse successfully captured the dynamic characteristics of conductivity changes with water content during growth and the significant differences between tissue structures, providing an effective absolute imaging method for non-invasive visualization and measurement of the physiological state of maize ears during the growing period.

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JBHI Journal 2025 Journal Article

Self-Supervised Contrastive Learning on Attribute and Topology Graphs for Predicting Relationships Among lncRNAs, miRNAs and Diseases

Lan Huang
Nan Sheng
Ling Gao
Lei Wang
Wenju Hou
Jie Hong
Yan Wang

Exploring associations between long non-coding RNAs (lncRNAs), microRNAs (miRNAs) and diseases is crucial for disease prevention, diagnosis and treatment. While determining these relationships experimentally is resource-intensive and time-consuming, computational methods have emerged as an attractive way. However, existing computational methods tend to focus on single tasks, neglecting the benefits of leveraging multiple biomolecular interactions and domain-specific knowledge for multi-task prediction. Furthermore, the scarcity of labeled data for lncRNA-disease associations (LDAs), miRNA-disease associations (MDAs) and lncRNA-miRNA interactions (LMIs) poses challenges for comprehensive node embedding learning. This paper proposes a multi-task prediction model (called SSCLMD) that employs self-supervised contrastive learning on attribute and topology graphs to identify potential LDAs, MDAs and LMIs. Firstly, domain knowledge of lncRNAs, miRNAs and diseases as well as their interactions are exploited to construct attribute graph and topology graph, respectively. Then, the nodes are encoded in the attribute and topology spaces to extract the specific and common feature. Meanwhile, the attention mechanism is performed to adaptively fuse the embedding from different views. SSCLMD incorporates contrastive self-supervised learning as a regularize to guide node embedding learning in both attribute and topology space without relying on labels. Severing as a regularize in multi-task learning paradigm, it to improves the model. s generalization capabilities. Extensive experiments on 2 manually curated datasets demonstrate that SSCLMD significantly outperforms baseline methods in LDA, MDA and LMI prediction tasks. Case studies on both old and new datasets further supported SSCLMD's ability to uncover novel disease-related lncRNAs and miRNAs.

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JBHI Journal 2025 Journal Article

TSLAmy: A Novel Amyloid Hexapeptide Aggregation Prediction Approach Based on Two-Stage Learning

Lan Huang
Qingchen Jiang
Yucong Xiong
Guangzhao Zhang
Dan Shao

Identifying aggregation-prone proteins or peptides is essential for advancing our understanding of amyloid aggregation processes and their related pathogenic mechanisms. Recognizing potential amyloid hexapeptides can also support peptide-based drug design and reduce experimental costs. In this study, we proposed TSLAmy, a computational model designed to predict amyloid hexapeptides using a two-stage learning framework. In the first stage, we performed feature extraction on the hexapeptides, and in the second stage, we presented prediction model for amyloid hexapeptide aggregation. Firstly, to ensure balanced dataset partitioning, we applied a clustering-based method by training two autoencoders on all possible hexapeptides using their sequence and physicochemical features, respectively. The resulting clusters were used to stratify the data into training and testing datasets. Then, in the first stage, we extracted features from hexapeptides based on their sequence and physicochemical properties. The feature extraction module was used to obtain physicochemical features, while the ESM-2 module was responsible for extracting sequence features for each hexapeptide. Finally, in the second stage, the aggregation prediction module was employed to predict the aggregation potential of hexapeptides. The experimental results demonstrated that the accuracy of TSLAmy reached 0. 8493 (0. 8447-0. 8539), outperforming other state-of-the-art methods. Furthermore, we predicted the aggregation potential of all 64, 000, 000 possible hexapeptides and analyzed the amino acids that form aggregation-prone hexapeptides. We anticipate that TSLAmy can offer new insights into the identification of aggregation-prone peptides, contributing to advancements in peptide drug development.

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AAAI Conference 2025 Conference Paper

WST: Wavelet-Based Multi-scale Tuning for Visual Transfer Learning

Jia Zeng
Lan Huang
Kangping Wang

Large-scale pre-trained Vision Transformer (ViT) models have demonstrated remarkable performance on visual tasks but are computationally expensive to transfer to downstream tasks. Parameter-Efficient Fine-Tuning (PEFT) offers a promising transferring approach by updating only a subset of parameters. However, PEFT's effectiveness is hindered by discrepancies between pre-training and downstream tasks in terms of object scale and granularity. Downstream tasks often focus on finer-grained and more specialized recognition, requiring more detailed features. The diversity of feature scales of existing PEFT methods for ViT is limited. To address this, we propose a novel PEFT method named Wavelet-based multi-Scale Tuning (WST), which learns multi-scale features in a simple and efficient way. WST introduces a parallel fine-tuning patch embedding branch with a smaller patch size than the pre-trained model to capture finer-grained features. Furthermore, to handle the computational challenge from the resulting longer token sequence, WST designs wavelet fine-tuning blocks that balance both efficiency and performance. In the block, wavelet transform enables invertible and lossless down-sampling of the longer token sequence, aligning it with that of the backbone, and two lightweight linear mappings are employed to learn task-specific features. This design facilitates efficient multi-scale information exchange between the pre-trained backbone and fine-tuning branch. Extensive experiments on transfer learning demonstrate the promising performance and efficiency of our WST.

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AAAI Conference 2024 Conference Paper

Improved Graph Contrastive Learning for Short Text Classification

Yonghao Liu
Lan Huang
Fausto Giunchiglia
Xiaoyue Feng
Renchu Guan

Text classification occupies an important role in natural language processing and has many applications in real life. Short text classification, as one of its subtopics, has attracted increasing interest from researchers since it is more challenging due to its semantic sparsity and insufficient labeled data. Recent studies attempt to combine graph learning and contrastive learning to alleviate the above problems in short text classification. Despite their fruitful success, there are still several inherent limitations. First, the generation of augmented views may disrupt the semantic structure within the text and introduce negative effects due to noise permutation. Second, they ignore the clustering-friendly features in unlabeled data and fail to further utilize the prior information in few valuable labeled data. To this end, we propose a novel model that utilizes improved Graph contrastIve learning for short text classiFicaTion (GIFT). Specifically, we construct a heterogeneous graph containing several component graphs by mining from an internal corpus and introducing an external knowledge graph. Then, we use singular value decomposition to generate augmented views for graph contrastive learning. Moreover, we employ constrained kmeans on labeled texts to learn clustering-friendly features, which facilitate cluster-oriented contrastive learning and assist in obtaining better category boundaries. Extensive experimental results show that GIFT significantly outperforms previous state-of-the-art methods. Our code can be found in https://github.com/KEAML-JLU/GIFT.

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EAAI Journal 2024 Journal Article

Prompt-based learning framework for zero-shot cross-lingual text classification

Kai Feng
Lan Huang
Kangping Wang
Wei Wei
Rui Zhang

Cross-lingual text classification is a challenging task that aims to train classifiers with data in one language, known as the source language, and apply the acquired knowledge to data in another language, referred to as the target language. Recent advancements in multilingual pre-trained language models (PLMs) have made significant progress in addressing cross-lingual issues, and the application of prompt-based learning has further improved task performance. However, these models still face challenges such as the gap between cross-lingual classification tasks and pre-training tasks of PLMs, as well as issues related to scarce resources and data noise, which hinder the full exploitation of the implicit knowledge in PLMs. In this paper, we propose a Prompt-based Cross-lingual Learning (PCL) framework that combines language-agnostic continuous prompt learning with self-learning process. Specifically, PCL framework leverages language-agnostic prompts and PLMs to achieve semantic transfer between source and target languages. To enhance the semantic relationship between prompts and category labels, a label attention module is introduced. Additionally, a set of self-training rules is proposed, which includes a scoring function. In a few-shot setting, noisy data is dynamically filtered through scoring and ranking of the data. During each training iteration, both the model and scoring function weights are updated, further improving the discrimination capability of the model. In summary, the proposed PCL framework builds upon cross-lingual prompt learning, effectively removing noisy data and applying it to zero-shot cross-lingual text classification, which is beneficial for engineering applications. The findings of this study have implications for prompt learning method. The PCL framework achieves state-of-the-art performance in cross-lingual text classification task, with a 14% performance improvement compared to basic soft prompt learning. This demonstrates its potential in addressing classification problems in resource-limited scenarios.

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IJCAI Conference 2024 Conference Paper

Resolving Word Vagueness with Scenario-guided Adapter for Natural Language Inference

Yonghao Liu
Mengyu Li
Di Liang
Ximing Li
Fausto Giunchiglia
Lan Huang
Xiaoyue Feng
Renchu Guan

Natural Language Inference (NLI) is a crucial task in natural language processing that involves determining the relationship between two sentences, typically referred to as the premise and the hypothesis. However, traditional NLI models solely rely on the semantic information inherent in independent sentences and lack relevant situational visual information, which can hinder a complete understanding of the intended meaning of the sentences due to the ambiguity and vagueness of language. To address this challenge, we propose an innovative ScenaFuse adapter that simultaneously integrates large-scale pre-trained linguistic knowledge and relevant visual information for NLI tasks. Specifically, we first design an image-sentence interaction module to incorporate visuals into the attention mechanism of the pre-trained model, allowing the two modalities to interact comprehensively. Furthermore, we introduce an image-sentence fusion module that can adaptively integrate visual information from images and semantic information from sentences. By incorporating relevant visual information and leveraging linguistic knowledge, our approach bridges the gap between language and vision, leading to improved understanding and inference capabilities in NLI tasks. Extensive benchmark experiments demonstrate that our proposed ScenaFuse, a scenario-guided approach, consistently boosts NLI performance.

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IJCAI Conference 2023 Conference Paper

Local and Global: Temporal Question Answering via Information Fusion

Yonghao Liu
Di Liang
Mengyu Li
Fausto Giunchiglia
Ximing Li
Sirui Wang
Wei Wu
Lan Huang

Many models that leverage knowledge graphs (KGs) have recently demonstrated remarkable success in question answering (QA) tasks. In the real world, many facts contained in KGs are time-constrained thus temporal KGQA has received increasing attention. Despite the fruitful efforts of previous models in temporal KGQA, they still have several limitations. (I) They neither emphasize the graph structural information between entities in KGs nor explicitly utilize a multi-hop relation path through graph neural networks to enhance answer prediction. (II) They adopt pre-trained language models (LMs) to obtain question representations, focusing merely on the global information related to the question while not highlighting the local information of the entities in KGs. To address these limitations, we introduce a novel model that simultaneously explores both Local information and Global information for the task of temporal KGQA (LGQA). Specifically, we first introduce an auxiliary task in the temporal KG embedding procedure to make timestamp embeddings time-order aware. Then, we design information fusion layers that effectively incorporate local and global information to deepen question understanding. We conduct extensive experiments on two benchmarks, and LGQA significantly outperforms previous state-of-the-art models, especially in difficult questions. Moreover, LGQA can generate interpretable and trustworthy predictions.

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