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Kaili Wang

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5 papers

2 author rows

JBHI Journal 2025 Journal Article

DRGCL: Drug Repositioning via Semantic-Enriched Graph Contrastive Learning

Xiao Jia
Xinliang Sun
Kaili Wang
Min Li

Drug repositioning greatly reduces drug development costs and time by discovering new indications for existing drugs. With the development of technology and large-scale biological databases, computational drug repositioning has increasingly attracted remarkable attention, which can narrow down repositioning candidates. Recently, graph neural networks (GNNs) have been widely used and achieved promising results in drug repositioning. However, the existing GNNs based methods usually focus on modeling the complex drug-disease association graph, but ignore the semantic information on the graph, which may lead to a lack of consistency of global topology information and local semantic information for the learned features. To alleviate the above challenge, we propose a novel drug repositioning model based on graph contrastive learning, termed DRGCL. First, we treat the known drug-disease associations as the topology graph. Second, we select the top- $K$ similar neighbor from drug/disease similarity information to construct the semantic graph rather than use the traditional data augmentation strategy, thereby maximally retaining rich semantic information. Finally, we pull closer to embedding consistency of the different embedding spaces by graph contrastive learning to enhance the topology and semantic feature on the graph. We have evaluated DRGCL on four benchmark datasets and the experiment results show that the proposed DRGCL is superior to the state-of-the-art methods. Especially, the average result of DRGCL is 11. 92% higher than that of the second-best method in terms of AUPRC. The case studies further demonstrate the reliability of DRGCL.

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JBHI Journal 2025 Journal Article

HCMAF: Hierarchical Feature Aggregation and Cross-Modal Attention Fusion Framework for Multi-Omics patient classification

Yanglan Gan
Hangkai Zhao
Kaili Wang
Cairong Yan
Guobing Zou

The accumulation of large-scale multi-omics datasets has brought new opportunities for precise disease treatment. However, the inherent complexity of inter- and intra-omics relationships presents considerable obstacles to the precise integration of multi-omics data. Here, we propose a Hierarchical Feature Aggregation and Cross-Modal Attention Fusion (HCMAF) framework to integrate multi-omics data for patient classification and biomarker identification. Specifically, to capture both the specific information inherent in each omics data and complex cross-omics interactions, HCMAF incorporates three innovative modules. The hierarchical feature aggregation graph attention (HGAT) module captures intra-omics topological features through adaptive neighborhood aggregation. The cross-modal attention (CMA) module pinpoints inter-omics complementarity by modeling cross-omics dependencies. Finally, the confidence-driven multi-omics fusion (CMF) module dynamically integrates omics-specific predictions through learnable reliability weights. Comprehensive experiments on four public benchmark datasets show that HCMAF achieves better classification performance and consistently surpasses leading existing methods. Further component analysis confirms the crucial role of the HGAT, CMA and CMF modules in ensuring overall model effectiveness. HCMAF is implemented in Python using the Pytorch machine-learning library, and it is freely available at https://github.com/DHUDBlab/HCMAF.

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EAAI Journal 2025 Journal Article

WA-Net: Wavelet Integrated Attention Network for Silk and Bamboo character recognition

Shengnan Li
Chi Zhou
Kaili Wang

Chu Bamboo and Silk ancient Chinese character (CBSC) was originated in the Chu state over 2000 years ago, representing an intermediate script between oracle bone script and seal script. Existing text images have degraded and suffered damage due to their ancient historical origins and insufficient preservation. Due to distinct structural and stroke texture characteristics, significant differences exist between CBSC and contemporary characters, posing challenges for intelligent recognition. Targeting these aforementioned characteristics, we propose a method called Wavelet Integrated Attention Network (WA-Net). This method integrates discrete wavelet transform and attention mechanisms to extract more discriminative features from severe noise interference and degraded text images. Additionally, a dataset named Chu Bamboo and Silk 730 (Chu730) for CBSC recognition has been created due to the lack of publicly available datasets. WA-Net introduces the discrete wavelet attention among layer (L-DWT) to broaden the feature learning space of convolutional neural networks into the wavelet domain, capturing latent information across various frequencies. Subsequently, a wavelet convolution (C-DWT) module is proposed to mitigate the partial information loss of conventional convolution operations. In the W-bneck module, the SE (Squeeze-and-Excitation) attention module and average pooling downsampling are introduced to enhance the extraction of valuable feature maps. Extensive experiments were conducted, including a baseline method that achieved top-1 recognition accuracy of 87. 42%. The proposed method achieved an accuracy of 89. 27%, and other top-n results also significantly surpassed the baseline accuracy. Other experiment results demonstrate the superiority of the proposed modules and theirvaluable applications in ancient text intelligent recognition and cultural heritage digital preservation. Furthermore, this approach holds significant promise in facilitating the study of other handwritten or ancient characters recognition. Dataset and code are available at: https: //github. com/Nancy45-ui/WA-Net.

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JBHI Journal 2023 Journal Article

Fusion-Based Deep Learning Architecture for Detecting Drug-Target Binding Affinity Using Target and Drug Sequence and Structure

Kaili Wang
Min Li

Accurately predicting drug-target binding affinity plays a vital role in accelerating drug discovery. Many computational approaches have been proposed due to costly and time-consuming of wet laboratory experiments. In the input representation, most methods only focus on the target sequence properties or target structure properties while ignore the overall contribution. Therefore, we develop a novel fusion protocol based on multiscale convolutional neural networks and graph neural networks, named CGraphDTA, to predict drug-target binding affinity using target sequence and structure. Unlike existing methods, CGraphDTA is the first model constructed with target sequence and structure as input. Concretely, the multiscale convolutional neural networks are utilized to extract target and drug presentation from sequence, graph neural networks are employed to extract graph presentation from target and drug molecular structure. We compare CGraphDTA with the state-of-the-art methods, the results show that our model outperforms the current methods on the test sets. Furthermore, we conduct ablation studies, biological interpretation examination and drug selectivity evaluation, all results suggest that CGraphDTA is a useful tool to predict drug-target binding affinity and accelerate drug discovery.

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ICRA Conference 2022 Conference Paper

Audio-Visual Grounding Referring Expression for Robotic Manipulation

Yefei Wang
Kaili Wang
Yi Wang
Di Guo 0002
Huaping Liu 0001
Fuchun Sun 0001

Referring expressions are commonly used when referring to a specific target in people's daily dialogue. In this paper, we develop a novel task of audio-visual grounding referring expression for robotic manipulation. The robot leverages both the audio and visual information to understand the referring expression in the given manipulation instruction and the corresponding manipulations are implemented. To solve the proposed task, an audio-visual framework is proposed for visual localization and sound recognition. We have also established a dataset which contains visual data, auditory data and manipulation instructions for evaluation. Finally, extensive experiments are conducted both offline and online to verify the effectiveness of the proposed audio-visual framework. And it is demonstrated that the robot performs better with the audio-visual data than with only the visual data.

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