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Liping Wang

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11 papers

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JBHI Journal 2026 Journal Article

Heterophily-Aware Spectral GCN for Population-Level Brain Disorder Prediction

Hao Zhang
Liping Wang
Yitian Zhao
Jianyang Xie
Tiyu Fang
Ran Song
Wei Zhang

Integrating resting-state functional magnetic resonance imaging (rs-fMRI) and phenotypic data is a promising way to build a comprehensive population graph for the prediction of brain disorders using graph neural networks (GNNs). However, existing GNN-based methods face two limitations: the complexity of relationships between subjects poses challenges in constructing a well-defined population graph, and the inherent node heterophily within the population graph is often overlooked. To address them, we propose a population graph with a phenotypic encoder, which leverages rs-fMRI and phenotypic data to model complex relationships between subjects and enables GNN to learn population-level features. We also design a heterophily-aware spectral graph convolution network that incorporates local similarity-based learning to assess node homophily and addresses the heterophily issue. Experiments demonstrate that our method performs well in classifying both Alzheimer's Disease and Autism Spectrum Disorder. In addition, it can distinguish between progressive and stable mild cognitive impairment, facilitating timely interventions for the diseases.

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JBHI Journal 2026 Journal Article

PathFusion-Net: A Rough Path Theory-Based Deep Learning Model for ECG Arrhythmia Classification

Tianlong Feng
Qingchen Li
Yuanyuan Zhang
Yongzhi Liao
Di Lu
Liping Wang
Jianqin Zhao
Lei Jiang

This study introduces a novel electrocardiogram (ECG) arrhythmia classification model, PathFusion-Net, which integrates Rough Path Theory with deep learning technologies. The model combines Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Path Signatures, and Path Development to extract spatial morphological features from ECG images and multi-order temporal representations from ECG signals. By adopting an inter-patient split paradigm, our approach more closely reflects real-world clinical diagnostic settings compared to intra-patient methods. The model demonstrates state-of-the-art overall classification performance on both the MIT-BIH Arrhythmia Database and a private clinical dataset, achieving 94. 7% and 95. 1% accuracy, respectively, under the AAMI four-class standard with an inter-patient split paradigm. On the MIT-BIH dataset, the proposed method attains competitive precision and recall across multiple arrhythmia types, including 95. 2% /87. 9% for ventricular ectopic beats (V) and 75. 7% /92. 3% for supraventricular ectopic beats (S), indicating balanced performance across clinically diverse categories. This research highlights the potential of Rough Path Theory in time-series analysis and offers a novel deep learning framework for automated early detection and monitoring of ECG arrhythmias. The code used in this study is available at: https://github.com/Rand2AI/PathFusion-Net.

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EAAI Journal 2025 Journal Article

Interpretable multi-source data fusion through Latent Variable Gaussian Process

Sandipp Krishnan Ravi
Yigitcan Comlek
Arjun Pathak
Vipul Gupta
Rajnikant Umretiya
Andrew Hoffman
Ghanshyam Pilania
Piyush Pandita

With the advent of artificial intelligence and machine learning, various domains of science and engineering communities have leveraged data-driven surrogates to model complex systems through fusing numerous sources of information (data) from published papers, patents, open repositories, or other available resources. However, very little attention has been paid to the differences in quality and comprehensiveness of the known and unknown underlying physical parameters of the information sources, which could have downstream implications during system optimization. Towards resolving this issue, an interpretable multi-source data fusion framework based on Latent Variable Gaussian Process (LVGP) model is proposed. The individual data sources are first labeled as categorical variables and then mapped into a physically meaningful latent space, enabling the development of a source-aware data fusion model. Additionally, a dissimilarity metric based on the learned latent variables of the LVGP is introduced to study and understand the differences between the data sources. The proposed approach is demonstrated on and analyzed through two mathematical and two materials engineering case studies. From the case studies, it is observed that the proposed multi-source data fusion framework provides more accurate predictions for sparse data scenarios compared to single-source or source-unaware data fusion models.

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EAAI Journal 2025 Journal Article

Predicting potential microbe-disease associations based on heterogeneous graph attention network and deep sparse autoencoder

Bo Wang
Wenlong Zhao
Xiaoxin Du
Jianfei Zhang
Chunyu Zhang
Liping Wang
Yang He

Identifying potential associations between microbes and diseases is crucial for explaining disease pathogenesis and designing targeted therapeutic strategies. Basic biological experiments for microbe-disease association (MDA) prediction are costly, time-consuming, and labor-intensive, whereas computational methods can effectively complement traditional biological experiments. We propose a computational framework called graph attention convolutional deep sparse autoencoder microbe-disease association (GCDSAEMDA) to predict unknown MDAs. First, we calculate the semantic similarity and Gaussian interaction profile (GIP) similarity of diseases, as well as the functional similarity and GIP similarity of microbes, and integrate these similarity matrices to construct a heterogeneous graph. Next, a multi-head dynamic graph attention mechanism is employed to extract low-order features of microbe and disease nodes in the heterogeneous graph, while multiple convolutional neural networks with different kernels aggregate and concatenate these low-order features to form new high-order representations. Third, we apply a cosine distance-based k-means clustering to select reliable negative samples and use a deep sparse autoencoder to extract high-order features of microbe-disease pairs. Finally, an ensemble Light Gradient Boosting Machine (LightGBM) algorithm is used to predict potential MDAs. GCDSAEMDA was compared to four state-of-the-art MDA models on the Human Microbe-Disease Association Database (HMDAD) and Disbiome databases and validated through five-fold cross-validation on diseases, microbes, and microbe-disease pairs. Results indicate that GCDSAEMDA outperforms the other four models in MDA prediction. Additionally, case studies demonstrate the robust predictive capability of GCDSAEMDA. The source code and datasets for GCDSAEMDA are available at https: //github. com/chenyunmolu/GCDSAEMDA.

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EAAI Journal 2023 Journal Article

A physics-informed neural network framework to predict 3D temperature field without labeled data in process of laser metal deposition

Shilin Li
Gang Wang
Yuelan Di
Liping Wang
Haidou Wang
Qingjun Zhou

To predict thermal behaviors during the laser metal deposition process, traditional approaches like experiments or finite-element methods(FEM) can be quite time-consuming, while data-driven machine learning models rely on large labeled datasets, which are too expensive to obtain. To fully exploit the potential of machine learning and release it from the dataset dependence, a physics-informed neural network framework that does not require any labeled data to predict 3D temperature field was proposed. The model used customized loss functions by replacing the original data loss with physical losses of heat conduction, convection and radiation. The implementation of nonlinear temperature-dependent material properties and the scaling of model inputs and outputs were involved. By iterative training, the model achieved accurate predictions of approximately 2% maximum relative error compared with FEM results. The transfer learning part was utilized for scenarios of different manufacturing parameters, and took about 1/3 of the calculation time as FEM did without losing accuracy. All the results above validated the high effectiveness and accuracy of the proposed framework.

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TCS Journal 2023 Journal Article

Nonlinear neural-like P model for time series classification

Xiyu Liu
Yuzhen Zhao
Liping Wang

Gated spiking neural P (GSNP) model is a novel recurrent model proposed recently, which is viewed as a variant of recurrent neural network. Time series classification (TSC) is one of the most challenging problems in data mining. In this work, based on the GSNP model, a nonlinear neural-like P (NNP) model for TSC is proposed. NNP model introduces the bidirectional mechanism and attention mechanism in deep learning, and builds nonlinear neuron modules together with GSNP to realize end-to-end learning. The neuron modules excavate the hidden features of time series to distinguish data and realize effective classification. Through evaluating on UCR Time Series Classification Archive, the classification performance of NNP model is significantly better than that of GSNP and other classification models. The NNP model can compete with advanced models on TSC task.

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IJCAI Conference 2022 Conference Paper

GraphDIVE: Graph Classification by Mixture of Diverse Experts

Fenyu Hu
Liping Wang
Qiang Liu
Shu Wu
Liang Wang
Tieniu Tan

Graph classification is a challenging research task in many applications across a broad range of domains. Recently, Graph Neural Network (GNN) models have achieved superior performance on various real-world graph datasets. Despite their successes, most of current GNN models largely suffer from the ubiquitous class imbalance problem, which typically results in prediction bias towards majority classes. Although many imbalanced learning methods have been proposed, they mainly focus on regular Euclidean data and cannot well utilize topological structure of graph (non-Euclidean) data. To boost the performance of GNNs and investigate the relationship between topological structure and class imbalance, we propose GraphDIVE, which learns multi-view graph representations and combine multi-view experts (i. e. , classifiers). Specifically, multi-view graph representations correspond to the intrinsic diverse graph topological structure characteristics. Extensive experiments on molecular benchmark datasets demonstrate the effectiveness of the proposed approach.

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TIST Journal 2020 Journal Article

Moment-Guided Discriminative Manifold Correlation Learning on Ordinal Data

Qing Tian
Wenqiang Zhang
Meng Cao
Liping Wang
Songcan Chen
Hujun Yin

Canonical correlation analysis (CCA) is a typical and useful learning paradigm in big data analysis for capturing correlation across multiple views of the same objects. When dealing with data with additional ordinal information, traditional CCA suffers from poor performance due to ignoring the ordinal relationships within the data. Such data is becoming increasingly common, as either temporal or sequential information is often associated with the data collection process. To incorporate the ordinal information into the objective function of CCA, the so-called ordinal discriminative CCA has been presented in the literature. Although ordinal discriminative CCA can yield better ordinal regression results, its performance deteriorates when data is corrupted with noise and outliers, as it tends to smear the order information contained in class centers. To address this issue, in this article we construct a robust manifold-preserved ordinal discriminative correlation regression (rmODCR). The robustness is achieved by replacing the traditional ( l 2 -norm) class centers with l p -norm centers, where p is efficiently estimated according to the moments of the data distributions, as well as by incorporating the manifold distribution information of the data in the objective optimization. In addition, we further extend the robust manifold-preserved ordinal discriminative correlation regression to deep convolutional architectures. Extensive experimental evaluations have demonstrated the superiority of the proposed methods.

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YNIMG Journal 2019 Journal Article

Representation of spatial sequences using nested rules in human prefrontal cortex

Liping Wang
Marie Amalric
Wen Fang
Xinjian Jiang
Christophe Pallier
Santiago Figueira
Mariano Sigman
Stanislas Dehaene

Memory for spatial sequences does not depend solely on the number of locations to be stored, but also on the presence of spatial regularities. Here, we show that the human brain quickly stores spatial sequences by detecting geometrical regularities at multiple time scales and encoding them in a format akin to a programming language. We measured gaze-anticipation behavior while spatial sequences of variable regularity were repeated. Participants’ behavior suggested that they quickly discovered the most compact description of each sequence in a language comprising nested rules, and used these rules to compress the sequence in memory and predict the next items. Activity in dorsal inferior prefrontal cortex correlated with the amount of compression, while right dorsolateral prefrontal cortex encoded the presence of embedded structures. Sequence learning was accompanied by a progressive differentiation of multi-voxel activity patterns in these regions. We propose that humans are endowed with a simple “language of geometry” which recruits a dorsal prefrontal circuit for geometrical rules, distinct from but close to areas involved in natural language processing.

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AIIM Journal 2016 Journal Article

A review on brain structures segmentation in magnetic resonance imaging

Sandra González-Villà
Arnau Oliver
Sergi Valverde
Liping Wang
Reyer Zwiggelaar
Xavier Lladó

Background and objectives Automatic brain structures segmentation in magnetic resonance images has been widely investigated in recent years with the goal of helping diagnosis and patient follow-up in different brain diseases. Here, we present a review of the state-of-the-art of automatic methods available in the literature ranging from structure specific segmentation methods to whole brain parcellation approaches. Methods We divide first the algorithms according to their target structures and then we propose a general classification based on their segmentation strategy, which includes atlas-based, learning-based, deformable, region-based and hybrid methods. We further discuss each category's strengths and weaknesses and analyze its performance in segmenting different brain structures providing a qualitative and quantitative comparison. Results We compare the results of the analyzed works for the following brain structures: hippocampus, thalamus, caudate nucleus, putamen, pallidum, amygdala, accumbens, lateral ventricles, and brainstem. The structures on which more works have focused on are the hippocampus and the caudate nucleus. In general, the accumbens (0. 69 mean DSC) is the most difficult structure to segment whereas the structures that seem to get the best results are the brainstem, closely followed by the thalamus and the putamen with 0. 88, 0. 87 and 0. 86 mean DSC, respectively. Atlas-based approaches achieve good results when segmenting the hippocampus (DSC between 0. 75 and 0. 90), thalamus (0. 88–0. 92) and lateral ventricles (0. 83–0. 93), while deformable methods perform good for caudate nucleus (0. 84–0. 91) and putamen segmentation (0. 86–0. 89). Conclusions There is not yet a single automatic segmentation approach that can emerge as a standard for the clinical practice, providing accurate brain structures segmentation. Future trends need to focus on combining multi-atlas methods with learning-based or deformable approaches. Employing atlases to provide spatial robustness and modeling the structures appearance with supervised classifiers or Active Appearance Models could lead to improved segmentation results.

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YNIMG Journal 2016 Journal Article

Direct detection of optogenetically evoked oscillatory neuronal electrical activity in rats using SLOE sequence

Yuhui Chai
Guoqiang Bi
Liping Wang
Fuqiang Xu
Ruiqi Wu
Xin Zhou
Bensheng Qiu
Hao Lei

The direct detection of neuronal electrical activity is one of the most challenging goals in non-BOLD fMRI research. Previous work has demonstrated its feasibility in phantom and cell culture studies, but attempts in in vivo studies remain few and far between. Most recent in vivo studies used T2*-weighted sequences to directly detect neuronal electrical activity evoked by sensory stimulus. As neuronal electrical signal is usually comprised of a series of spectrally distributed oscillatory waveforms rather than being a direct current, it is most likely to be detected using oscillatory current sensitive sequences. In this study, we explored the potential of using the spin-lock oscillatory excitation (SLOE) sequence with spiral readout to directly detect optogenetically evoked oscillatory neuronal electrical activity, whose main spectral component can be manipulated artificially to match the resonance frequency of spin-lock RF field. In addition, experiments using the stimulus-induced rotary saturation (SIRS) sequence with spiral readout were also performed. Electrophysiological recording and MRI data acquisition were conducted on separate animals. Robust optogenetically evoked oscillatory LFP signals were observed and significant BOLD signals were acquired with the GE-EPI sequence before and after the whole SLOE and SIRS acquisitions, but no significant neuronal current MRI (ncMRI) signal changes were detected. These results indicate that the sensitivity of oscillatory current sensitive sequences needs to be further improved for direct detection of neuronal electrical activity.

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