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Pei Liu

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9 papers

2 author rows

EAAI Journal 2025 Journal Article

A single-cell RNA sequencing data imputation method based on non-negative matrix factorization and multi-kernel similarity network fusion

Pei Liu
Cheng Chen
Hao Liu
Jin Gu
Xinya Chen
Ying Su
Zhiyuan Cheng
Xiaoyi Lv

Artificial intelligence-based single-cell RNA sequencing (scRNA-seq) technology is widely used in cell type identification and disease research, but its data often contain a large number of missing values and zero values due to technical limitations and biological differences. These zero values not only affect downstream analysis, but also make it difficult to distinguish technical zero values from biological zero values. Therefore, this paper proposes a scRNA-seq data interpolation method (sc-MKNMF) based on non-negative matrix factorization and multi-kernel similarity network fusion for the first time. This method improves the accuracy of cell clustering by accurately filling some zero values. First, sc-MKNMF uses gene-cell dual-level analysis to distinguish technical zero values from biological zero values, and then calculates the similarity network of multi-kernel fusion of genes and cells respectively. Then, this method uses non-negative matrix factorization combined with similarity network to construct the objective function, and introduces sparse regularization terms to ensure the similarity between genes and cells and improve stability. In addition, sc-MKNMF is also equipped with an efficient optimization algorithm to promote its convergence by continuously updating the objective function. Finally, the verification and comparative experiments on 12 scRNA-seq datasets show that the sc-MKNMF method outperforms other advanced data interpolation methods. In addition, the extension of sc-MKNMF to the two tasks of cell trajectory inference and differentially expressed gene analysis showed significant improvement and excellent versatility.

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AAAI Conference 2025 Conference Paper

Mining In-distribution Attributes in Outliers for Out-of-distribution Detection

Yutian Lei
Luping Ji
Pei Liu

Out-of-distribution (OOD) detection is indispensable for deploying reliable machine learning systems in real-world scenarios. Recent works, using auxiliary outliers in training, have shown good potential. However, they seldom concern the intrinsic correlations between in-distribution (ID) and OOD data. In this work, we discover an obvious correlation that OOD data usually possesses significant ID attributes. These attributes should be factored into the training process, rather than blindly suppressed as in previous approaches. Based on this insight, we propose a structured multi-view-based out-of-distribution detection learning (MVOL) framework, which facilitates rational handling of the intrinsic in-distribution attributes in outliers. We provide theoretical insights on the effectiveness of MVOL for OOD detection. Extensive experiments demonstrate the superiority of our framework to others. MVOL effectively utilizes both auxiliary OOD datasets and even wild datasets with noisy ID data.

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AAAI Conference 2025 Conference Paper

Queryable Prototype Multiple Instance Learning with Vision-Language Models for Incremental Whole Slide Image Classification

Jiaxiang Gou
Luping Ji
Pei Liu
Mao Ye

Whole Slide Image (WSI) classification has very significant applications in clinical pathology, e.g., tumor identification and cancer diagnosis. Currently, most research attention is focused on Multiple Instance Learning (MIL) using static datasets. One of the most obvious weaknesses of these methods is that they cannot efficiently preserve and utilize previously learned knowledge. With any new data arriving, classification models are required to be re-trained on both previous and current new data. To overcome this shortcoming and break through traditional vision modality, this paper proposes the first Vision-Language-based framework with Queryable Prototype Multiple Instance Learning (QPMIL-VL) specially designed for incremental WSI classification. This framework mainly consists of two information processing branches: one is for generating bag-level features by prototype-guided aggregation of instance features, while the other is for enhancing class features through a combination of class ensemble, tunable vector and class similarity loss. The experiments on four public WSI datasets demonstrate that our QPMIL-VL framework is effective for incremental WSI classification and often significantly outperforms other compared methods, achieving state-of-the-art (SOTA) performance.

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ICRA Conference 2025 Conference Paper

Scene-Aware Explainable Multimodal Trajectory Prediction

Pei Liu
Haipeng Liu
Xingyu Liu
Yiqun Li
Junlan Chen
Yangfan He
Jun Ma 0008

Advancements in intelligent technologies have significantly improved navigation in complex traffic environments by enhancing environment perception and trajectory prediction for automated vehicles. However, current research often overlooks the joint reasoning of scenario agents and lacks explainability in trajectory prediction models, limiting their practical use in real-world situations. To address this, we introduce the Explainable Conditional Diffusion-based Multimodal Trajectory Prediction (DMTP) model, which is designed to elucidate the environmental factors influencing predictions and reveal the underlying mechanisms. Our model integrates a modified conditional diffusion approach to capture multimodal trajectory patterns and employs a revised Shapley Value model to assess the significance of global and scenario-specific features. Experiments using the Waymo Open Motion Dataset demonstrate that our explainable model excels in identifying critical inputs and significantly outperforms baseline models in accuracy. Moreover, the factors identified align with the human driving experience, underscoring the model's effectiveness in learning accurate predictions. Code is available in our open-source repository: https://github.com/ocean-luna/Explainable-Prediction.

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AIIM Journal 2025 Journal Article

TDMFS: Tucker decomposition multimodal fusion model for pan-cancer survival prediction

Jinchao Chen
Pei Liu
Chen Chen
Ying Su
Enguang Zuo
Min Li
Jiajia Wang
Ziwei Yan

Integrated analysis of multimodal data offers a more comprehensive view for cancer survival prediction, yet it faces challenges like computational intensity, overfitting, and challenges in achieving a unified representation due to data heterogeneity. To address the above issues, the first Tucker decomposition multimodal fusion model was hereby proposed for pan-cancer survival prediction (TDMFS). The model employed Tucker decomposition to limit complex tensor parameters during fusion, achieving deep modality integration with reduced computational cost and lower overfitting risk. The individual modality-specific representations were then fully exploited by signal modulation mechanisms in a bilinear pooling decomposition to serve as complementary information for the deep fusion representation. Furthermore, the performance of TDMFS was evaluated using a 5-fold cross-validation method with two modal data, gene expression (GeneExpr), and copy number variation (CNV), for 33 cancers from The Cancer Genome Atlas (TCGA) database. The experiments demonstrated that the proposed TDMFS model achieved an average C-index of 0. 757 across 33 cancer datasets, with a C-index exceeding 0. 80 on 10 of these datasets. Survival curves for both high and low risk patients plotted on 27 cancer datasets were statistically significant. The TDMFS model demonstrated superior performance in survival prediction, outperforming models like LinearSum and Multimodal Factorisation Higher Order Pooling, making it a valuable asset for advancing clinical cancer research.

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ICRA Conference 2025 Conference Paper

Wcdt: World-Centric Diffusion Transformer for Traffic Scene Generation

Chen Yang
Yangfan He
Aaron Xuxiang Tian
Dong Chen 0016
Jianhui Wang
Tianyu Shi
Arsalan Heydarian
Pei Liu

In this paper, we introduce a novel approach for autonomous driving trajectory generation by harnessing the complementary strengths of diffusion probabilistic models (a. k. a. , diffusion models) and transformers. Our proposed framework, termed the “World-centric Diffusion Transformer” (WcDT), optimizes the entire trajectory generation process, from feature extraction to model inference. To enhance the scene diversity and stochasticity, the historical trajectory data is first preprocessed into “Agent Move Statement” and encoded into latent space using Denoising Diffusion Probabilistic Models (DDPM) enhanced with Diffusion with Transformer (DiT) blocks. Then, the latent features, historical trajectories, HD map features, and historical traffic signal information are fused with various transformer-based encoders that is used to enhance the interaction of agents with other elements in the traffic scene. The encoded traffic scenes are then decoded by a trajectory decoder to generate multimodal future trajectories. Comprehensive experimental results show that the proposed approach exhibits superior performance in generating both realistic and diverse trajectories, showing its potential for integration into automatic driving simulation systems. Our code is available at https://github.com/yangchen1997/WcDT.

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AAAI Conference 2024 Conference Paper

DiffRAW: Leveraging Diffusion Model to Generate DSLR-Comparable Perceptual Quality sRGB from Smartphone RAW Images

Mingxin Yi
Kai Zhang
Pei Liu
Tanli Zuo
Jingduo Tian

Deriving DSLR-quality sRGB images from smartphone RAW images has become a compelling challenge due to discernible detail disparity, color mapping instability, and spatial misalignment in RAW-sRGB data pairs. We present DiffRAW, a novel method that incorporates the diffusion model for the first time in learning RAW-to-sRGB mappings. By leveraging the diffusion model, our approach effectively learns the high-quality detail distribution of DSLR images, thereby enhancing the details of output images. Simultaneously, we use the RAW image as a diffusion condition to maintain image structure information such as contours and textures. To mitigate the interference caused by the color and spatial misalignment in training data pairs, we embed a color-position preserving condition within DiffRAW, ensuring that the output images do not exhibit color biases and pixel shift issues. To accelerate the inference process of DiffRAW, we designed the Domain Transform Diffusion Method, an efficient diffusion process with its corresponding reverse process. The Domain Transform Diffusion Method can reduce the required inference steps for diffusion model-based image restoration/enhancement algorithms while enhancing the quality of the generated images. Through evaluations on the ZRR dataset, DiffRAW consistently demonstrates state-of-the-art performance across all perceptual quality metrics (e.g., LPIPS, FID, MUSIQ), while achieving comparable results in PSNR and SSIM.

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AAAI Conference 2024 Conference Paper

ModWaveMLP: MLP-Based Mode Decomposition and Wavelet Denoising Model to Defeat Complex Structures in Traffic Forecasting

Ke Sun
Pei Liu
Pengfei Li
Zhifang Liao

Traffic prediction is the core issue of Intelligent Transportation Systems. Recently, researchers have tended to use complex structures, such as transformer-based structures, for tasks such as traffic prediction. Notably, traffic data is simpler to process compared to text and images, which raises questions about the necessity of these structures. Additionally, when handling traffic data, researchers tend to manually design the model structure based on the data features, which makes the structure of traffic prediction redundant and the model generalizability limited. To address the above, we introduce the ‘ModWaveMLP’—A multilayer perceptron (MLP) based model designed according to mode decomposition and wavelet noise reduction information learning concepts. The model is based on simple MLP structure, which achieves the separation and prediction of different traffic modes and does not depend on additional features introduced such as the topology of the traffic network. By performing experiments on real-world datasets METR-LA and PEMS-BAY, our model achieves SOTA, outperforms GNN and transformer-based models, and outperforms those that introduce additional feature data with better generalizability, and we further demonstrate the effectiveness of the various parts of the model through ablation experiments. This offers new insights to subsequent researchers involved in traffic model design. The code is available at: https://github.com/Kqingzheng/ModWaveMLP.

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JBHI Journal 2022 Journal Article

Data Integration Using Tensor Decomposition for the Prediction of miRNA-Disease Associations

Jiawei Luo
Yi Liu
Pei Liu
Zihan Lai
Hao Wu

Dysfunction of miRNAs has an important relationship with diseases by impacting their target genes. Identifying disease-related miRNAs is of great significance to prevent and treat diseases. Integrating information of genes related miRNAs and/or diseases in calculational methods for miRNA-disease association studies is meaningful because of the complexity of biological mechanisms. Therefore, in this study, we propose a novel method based on tensor decomposition, termed TDMDA, to integrate multi-type data for identifying pathogenic miRNAs. First, we construct a three-order association tensor to express the associations of miRNA-disease pairs, the associations of miRNA-gene pairs, and the associations of gene-disease pairs simultaneously. Then, a tensor decomposition-based method with auxiliary information is applied to reconstruct the association tensor for predicting miRNA-disease associations, and the auxiliary information includes biological similarity information and adjacency information. The performance of TDMDA is compared with other advanced methods under 5-fold cross-validations. The experimental results indicate the TDMDA is a competitive method.

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