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Wentao Cui

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4 papers

2 author rows

JBHI Journal 2025 Journal Article

Hierarchical Graph Transformer With Contrastive Learning for Gene Regulatory Network Inference

Wentao Cui
Qingqing Long
Wenhao Liu
Chen Fang
Xuezhi Wang
Pengfei Wang
Yuanchun Zhou

Gene regulatory networks (GRNs) are crucial for understanding gene regulation and cellular processes. Inferring GRNs helps uncover regulatory pathways, shedding light on the regulation and development of cellular processes. With the rise of high-throughput sequencing and advancements in computational technology, computational models have emerged as cost-effective alternatives to traditional experimental studies. Moreover, the surge in ChIP-seq data for TF-DNA binding has catalyzed the development of graph neural network (GNN)-based methods, greatly advancing GRN inference capabilities. However, most existing GNN-based methods suffer from the inability to capture long-distance structural semantic correlations due to transitive interactions. In this paper, we introduce a novel GNN-based model named H ierarchical G raph T ransformer with C ontrastive Learning for GRN (HGTCGRN) inference. HGTCGRN excels at capturing structural semantics using a hierarchical graph Transformer, which introduces a series of gene family nodes representing gene functions as virtual nodes to interact with nodes in the GRNS. These semantic-aware virtual-node embeddings are aggregated to produce node representations with varying emphasis. Additionally, we leverage gene ontology information to construct gene interaction networks for contrastive learning optimization of GRNs. Experimental results demonstrate that HGTCGRN achieves superior performance in GRN inference.

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AAAI Conference 2023 Conference Paper

Contrastive Learning with the Feature Reconstruction Amplifier

Wentao Cui
Liang Bai

Contrastive learning has emerged as one of the most promising self-supervised methods. It can efficiently learn the transferable representations of samples through the instance-level discrimination task. In general, the performance of the contrastive learning method can be further improved by projecting the transferable high-dimensional representations into the low-dimensional feature space. This is because the model can learn more abstract discriminative information. However, when low-dimensional features cannot provide sufficient discriminative information to the model (e.g., the samples are very similar to each other), the existing contrastive learning method will be limited to a great extent. Therefore, in this paper, we propose a general module called the Feature Reconstruction Amplifier (FRA) for adding additional high-dimensional feature information to the model. Specifically, FRA reconstructs the low-dimensional feature embeddings with Gaussian noise vectors and projects them to a high-dimensional reconstruction space. In this reconstruction space, we can add additional feature information through the designed loss. We have verified the effectiveness of the module itself through exhaustive ablation experiments. In addition, we perform linear evaluation and transfer learning on five common visual datasets, the experimental results demonstrate that our method is superior to recent advanced contrastive learning methods.

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JBHI Journal 2021 Journal Article

Lung Respiratory Motion Estimation Based on Fast Kalman Filtering and 4D CT Image Registration

Peng Xue
Yu Fu
Huizhong Ji
Wentao Cui
Enqing Dong

Respiratory motion estimation is an important part in image-guided radiation therapy and clinical diagnosis. However, most of the respiratory motion estimation methods rely on indirect measurements of external breathing indicators, which will not only introduce great estimation errors, but also bring invasive injury for patients. In this paper, we propose a method of lung respiratory motion estimation based on fast Kalman filtering and 4D CT image registration (LRME-4DCT). In order to perform dynamic motion estimation for continuous phases, a motion estimation model is constructed by combining two kinds of GPU-accelerated 4D CT image registration methods with fast Kalman filtering method. To address the high computational requirements of 4D CT image sequences, a multi-level processing strategy is adopted in the 4D CT image registration methods, and respiratory motion states are predicted from three independent directions. In the DIR-lab dataset and POPI dataset with 4D CT images, the average target registration error (TRE) of the LRME-4DCT method can reach 0. 91 mm and 0. 85 mm respectively. Compared with traditional estimation methods based on pair-wise image registration, the proposed LRME-4DCT method can estimate the physiological respiratory motion more accurately and quickly. Our proposed LRME-4DCT method fully meets the practical clinical requirements for rapid dynamic estimation of lung respiratory motion.

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ICML Conference 2021 Conference Paper

Parameterless Transductive Feature Re-representation for Few-Shot Learning

Wentao Cui
Yuhong Guo

Recent literature in few-shot learning (FSL) has shown that transductive methods often outperform their inductive counterparts. However, most transductive solutions, particularly the meta-learning based ones, require inserting trainable parameters on top of some inductive baselines to facilitate transduction. In this paper, we propose a parameterless transductive feature re-representation framework that differs from all existing solutions from the following perspectives. (1) It is widely compatible with existing FSL methods, including meta-learning and fine tuning based models. (2) The framework is simple and introduces no extra training parameters when applied to any architecture. We conduct experiments on three benchmark datasets by applying the framework to both representative meta-learning baselines and state-of-the-art FSL methods. Our framework consistently improves performances in all experiments and refreshes the state-of-the-art FSL results.

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