Label Propagation Model Research Articles

TIWMFLP: Two-Tier Interactive Weighted Matrix Factorization and Label Propagation Based on Similarity Matrix Fusion for Drug-Disease Association Prediction.

Accurately identifying new therapeutic uses for drugs is crucial for advancing pharmaceutical research and development. Matrix factorization is often used in association prediction due to its simplicity and high interpretability. However, existing matrix factorization models do not enable real-time interaction between molecular feature matrices and similarity matrices, nor do they consider the geometric structure of the matrices. Additionally, efficiently integrating multisource data remains a significant challenge. To address these issues, we propose a two-tier interactive weighted matrix factorization and label propagation model based on similarity matrix fusion (TIWMFLP) to assist in personalized treatment. First, we calculate the Gaussian and Laplace kernel similarities for drugs and diseases using known drug-disease associations. We then introduce a new multisource similarity fusion method, called similarity matrix fusion (SMF), to integrate these drug/disease similarities. SMF not only considers the different contributions represented by each neighbor but also incorporates drug-disease association information to enhance the contextual topological relationships and potential features of each drug/disease node in the network. Second, we innovatively developed a two-tier interactive weighted matrix factorization (TIWMF) method to process three biological networks. This method realizes for the first time the real-time interaction between the drug/disease feature matrix and its similarity matrix, allowing for a better capture of the complex relationships between drugs and diseases. Additionally, the weighted matrix of the drug/disease similarity matrix is introduced to preserve the underlying structure of the similarity matrix. Finally, the label propagation algorithm makes predictions based on the three updated biological networks. Experimental outcomes reveal that TIWMFLP consistently surpasses state-of-the-art models on four drug-disease data sets, two small molecule-miRNA data sets, and one miRNA-disease data set.

Using Multiple Drug Similarity Networks to Promote Adverse Drug Event Detection

The occurrence of an adverse drug event (ADE) has become a serious social concern of public health. Early detection of ADEs can lower the risk of drug safety as well as the expense of the drug. While post-market spontaneous reports of ADEs remain a cornerstone of pharmacovigilance, most existing signal detection algorithms rely on substantial accumulated data, limiting their applicability to early ADE detection when reports are scarce. To address this issue, we propose a label propagation model for generating enhanced drug safety signals using multiple drug features. We first construct multiple drug similarity networks using a range of drug features. We then calculate initial drug safety signals using conventional signal detection algorithms. These original signals are subsequently propagated across each drug similarity network to obtain enhanced drug safety signals. We evaluate our proposed model using two common signal detection algorithms on data from the FDA Adverse Event Reporting System (FAERS). Results demonstrate that enhanced drug safety signals with pre-clinical information outperform the standard safety signal detection algorithms on early ADE detection. In addition, we systematically evaluate the performance of different drug similarities against different types of ADEs. Furthermore, we have developed a web interface (http://drug-drug-sim.aimedlab.net/) to display our multiple drug similarity scores, facilitating access to this valuable resource for drug safety monitoring.

Attention guided multi-wavelet adversarial network for cross domain fault diagnosis

Adversarial transfer learning is an effective method for diagnosing rotating machinery faults. However, it does not extract features completely and underutilizes unlabeled target data. Hence, an attention-guided multi-wavelet adversarial network (AMWAN) based on the hybrid metric strategy of a multi-wavelet convolutional feature extractor is proposed. First, hidden fault features in the input data are extracted via multi-wavelet convolution and an attention mechanism. Subsequently, the extracted features are input into the domain discriminator and label classifier. A gradient reversal layer is introduced to map and mix the source- and target- domain samples for feature extraction. Specifically, the k-nearest neighbor is adopted to construct a label propagation model in the source-domain feature layer to obtain pseudo-labels for the target-domain feature data. This extends the labeling information for the target-domain feature data. A hybrid metric based on the maximum mean discrepancy distance and feature cosine metric strategy is introduced in the feature layer for adversarial training, which can significantly enhance the domain adaptive effect and improve the cross-domain fault diagnosis performance of the AMWAN. Experimental validation based on two sets of planetary gearbox datasets indicate that the AMWAN exhibits outstanding fault diagnosis performance under both constant- and variable-speed and -load conditions, i.e., it achieves an average accuracy rate of up to 96.85 %, thus surpassing other methods.

Graph inference algorithms as high-performance tools for detecting geochemical anomalies related to mineralization from geochemical exploration data

Geochemical exploration data usually consists of a small number of labeled data (cells containing known deposits) and a large number of unlabeled data (cells containing none of known deposits). How to make full use of a small number of labeled data and a large number of unlabeled data to build a high-performance anomaly detection model for identifying mineralization anomalies from geochemical exploration data is a challenge faced by geochemical exploration practitioners. The graph inference algorithms including label propagation and label spreading can efficiently build high-performance semi-supervised classification models by learning from sparse labeled and abundant unlabeled data in machine learning. For this reason, the two graph inference algorithms were used to build the semi-supervised classification models to detect Fe-mineralization anomalies from stream sediment survey data of 1: 50,000 scale in the Chengde area (Hebei Province, China). The label propagation model and label spreading model were compared with the Gaussian mixture model and logistic regression model in detecting Fe-mineralization anomalies. The data processing time of the label propagation algorithm and label spreading algorithm is 64.26 s and 28.21 s, respectively; and the data processing time of the Gaussian mixture model and logistic regression algorithm is 37.79 s and 18.98 s, respectively. The area under the receiver operating characteristic curves (AUCs) of the label propagation model and label spreading model are 0.979 and 0.999, respectively; and the AUCs of the Gaussian mixture model and logistic regression model are 0.800 and 0.817, respectively. Therefore, the data modeling efficiency of the label propagation model and label spreading model is comparable to that of the Gaussian mixture model and logistic regression model in detecting Fe-mineralization anomalies. However, the performance of the label propagation model and label spreading model are significantly better than that of the Gaussian mixture model and logistic regression model. It can be concluded that the two graph inference algorithms are potentially useful high-performance tools for detecting mineralization anomalies from geochemical exploration data.

NetPro: Neighborhood Interaction-based Drug Repositioning via Label Propagation.

Drug repositioning is an important approach for predicting new disease indications of the existing drugs in drug discovery. A great progress has been achieved in drug repositioning. However, effectively utilizing the localized neighborhood interaction features of drug and disease in drug-disease associations remains challenging. This paper proposes a neighborhood interaction-based method called NetPro for drug repositioning via label propagation. In NetPro, we first formulate the known drug-disease associations, various disease and drug similarities from different perspectives to construct drug-drug and disease-disease networks. Meanwhile we employ the nearest neighbors and their interactions in the constructed networks to devise a new approach for computing drug similarity and disease similarity. To implement the prediction of new drugs or diseases, a preprocessing step is applied to renew the known drug-disease associations using our calculated drug and disease similarities. We then employ a label propagation model to predict drug-disease associations by the drug and disease linear neighborhood similarities derived from the renewed drug-disease associations. The experimental results on three benchmark datasets show that NetPro can effectively identify potential drug-disease associations and achieve better prediction performance than the existing methods. Case studies further demonstrate that NetPro is capable of predicting promising candidate disease indications for drugs.

Inferior Alveolar Canal Automatic Detection with Deep Learning CNNs on CBCTs: Development of a Novel Model and Release of Open-Source Dataset and Algorithm

Introduction: The need of accurate three-dimensional data of anatomical structures is increasing in the surgical field. The development of convolutional neural networks (CNNs) has been helping to fill this gap by trying to provide efficient tools to clinicians. Nonetheless, the lack of a fully accessible datasets and open-source algorithms is slowing the improvements in this field. In this paper, we focus on the fully automatic segmentation of the Inferior Alveolar Canal (IAC), which is of immense interest in the dental and maxillo-facial surgeries. Conventionally, only a bidimensional annotation of the IAC is used in common clinical practice. A reliable convolutional neural network (CNNs) might be timesaving in daily practice and improve the quality of assistance. Materials and methods: Cone Beam Computed Tomography (CBCT) volumes obtained from a single radiological center using the same machine were gathered and annotated. The course of the IAC was annotated on the CBCT volumes. A secondary dataset with sparse annotations and a primary dataset with both dense and sparse annotations were generated. Three separate experiments were conducted in order to evaluate the CNN. The IoU and Dice scores of every experiment were recorded as the primary endpoint, while the time needed to achieve the annotation was assessed as the secondary end-point. Results: A total of 347 CBCT volumes were collected, then divided into primary and secondary datasets. Among the three experiments, an IoU score of 0.64 and a Dice score of 0.79 were obtained thanks to the pre-training of the CNN on the secondary dataset and the creation of a novel deep label propagation model, followed by proper training on the primary dataset. To the best of our knowledge, these results are the best ever published in the segmentation of the IAC. The datasets is publicly available and algorithm is published as open-source software. On average, the CNN could produce a 3D annotation of the IAC in 6.33 s, compared to 87.3 s needed by the radiology technician to produce a bidimensional annotation. Conclusions: To resume, the following achievements have been reached. A new state of the art in terms of Dice score was achieved, overcoming the threshold commonly considered of 0.75 for the use in clinical practice. The CNN could fully automatically produce accurate three-dimensional segmentation of the IAC in a rapid setting, compared to the bidimensional annotations commonly used in the clinical practice and generated in a time-consuming manner. We introduced our innovative deep label propagation method to optimize the performance of the CNN in the segmentation of the IAC. For the first time in this field, the datasets and the source codes used were publicly released, granting reproducibility of the experiments and helping in the improvement of IAC segmentation.

Learning label diffusion maps for semi-automatic segmentation of lung CT images with COVID-19

Deep Learning (DL) has become one of the key approaches for dealing with many challenges in medical imaging, which includes lung segmentation in Computed Tomography (CT). The use of seeded segmentation methods is another effective approach to get accurate partitions from complex CT images, as they give users autonomy, flexibility and easy usability when selecting specific targets for measurement purposes or pharmaceutical interventions. In this paper, we combine the accuracy of deep contour leaning with the versatility of seeded segmentation to yield a semi-automatic framework for segmenting lung CT images from patients affected by COVID-19. More specifically, we design a DL-driven approach that learns label diffusion maps from a contour detection network integrated with a label propagation model, used to diffuse the seeds over the CT images. Moreover, the trained model induces the diffusion of the seeds by only taking as input a marked CT-scan, segmenting hundreds of CT slices in an unsupervised and recursive way. Another important trait of our framework is that it is capable of segmenting lung structures even in the lack of well-defined boundaries and regardless of the level of COVID-19 infection. The accuracy and effectiveness of our learned diffusion model are attested to by both qualitative as well as quantitative comparisons involving several user-steered segmentations methods and eight CT data sets containing different types of lesions caused by COVID-19.

Joint Feature Adaptation and Graph Adaptive Label Propagation for Cross-Subject Emotion Recognition From EEG Signals

Though Electroencephalogram (EEG) could objectively reflect emotional states of our human beings, its weak, non-stationary, and low signal-to-noise properties easily cause the individual differences. To enhance the universality of affective brain-computer interface systems, transfer learning has been widely used to alleviate the data distribution discrepancies among subjects. However, most of existing approaches focused mainly on the domain-invariant feature learning, which is not unified together with the recognition process. In this paper, we propose a joint feature adaptation and graph adaptive label propagation model (JAGP) for cross-subject emotion recognition from EEG signals, which seamlessly unifies the three components of domain-invariant feature learning, emotional state estimation and optimal graph learning together into a single objective. We conduct extensive experiments on two benchmark SEED_IV and SEED_V data sets and the results reveal that 1) the recognition performance is greatly improved, indicating the effectiveness of the triple unification mode; 2) the emotion metric of EEG samples are gradually optimized during model training, showing the necessity of optimal graph learning, and 3) the projection matrix-induced feature importance is obtained based on which the critical frequency bands and brain regions corresponding to subject-invariant features can be automatically identified, demonstrating the superiority of the learned shared subspace.

SDNMF: Semisupervised discriminative nonnegative matrix factorization for feature learning

As one of the most effective feature learning methods, Nonnegative Matrix Factorization (NMF) has been widely used in many scientific fields, such as computer vision, data mining, and bioinformatics. However, NMF is an unsupervised method that cannot fully utilize the label information of data. Thus, its performance is limited in some recognition and classification problems. To remedy this shortcoming, this paper proposes a Semisupervised Discriminative NMF (SDNMF) method. First, we design a Soft-Labeled NMF (SLNMF) model by introducing a soft-label matrix-based regression term into the original NMF, so that the relationship between the soft-label matrix and low-dimensional features can be constructed to improve the discriminative ability of low-dimensional features. Second, to effectively estimate the soft-label matrix, a Label Propagation (LP) model is adopted to fully explore the spatial distribution relationship between the labeled and unlabeled samples. Third, an Adaptive Graph Learning (AGL) model is proposed to exploit the geometric relationship of samples well, which could enhance the performance of LP. Finally, the above three models (i.e., SLNMF, LP, and AGL) are integrated into a unified framework for effective feature learning, which can not only effectively explore the structural relationship matrix between data, but also predict the labels for unknown samples. Moreover, an iterative optimization algorithm is presented to solve our objective function. The convergence and computational complexity analysis of the proposed SDNMF method are also provided. Extensive experiments are conducted on several standard data sets. Compared with related methods, the experimental results verify that the proposed SDNMF method achieves better performance.

Monocular Depth Matching With Hybrid Sampling and Depth Label Propagation

This paper proposes a monocular depth label propagation model, which describes monocular images into depth label distribution for the target classification matching. 1) Depth label propagation by hybrid sampling and salient region sifting, improve the discrimination of detection feature categories. 2) Depth label mapping and spectrum clustering to classify target, define the depth of the sorting rules. The experimental results of motion recognition and 3D point cloud processing, show that this method can approximately reach the performance of all previous monocular depth estimation methods. The neural network model black box training learning module is not used, which improves the interpretability of the proposed model.

Adaptive multi-scale transductive information propagation for few-shot learning

Few-shot learning aims to learn a classifier with more generalization capability from extremely limited labeled samples has drawn an increasing amount of attention in many areas. One typical work in this field is the transductive propagation network (TPN), which propagates labels by capturing the local geometry distribution information between data. However, TPN exploited only the manifold structure of feature extractor networks’ high-layer semantic features (single-scale) distribution, while neglecting their local geometry distribution in the low-layer semantic features. This paper presents the adaptive multi-scale transductive information propagation (AMTIP) model to address this problem. Specifically, we introduce the multi-scale feature extractor networks to simultaneously learn samples’ high-layer global semantic features and low-layer local semantic features. After acquiring the multi-scale features, we propose the adaptive multi-scale fusion networks to generate the adaptive semantic fusion features applying to different few-shot classification tasks. Finally, the adaptive semantic fusion features are applied to the label propagation model for few-shot image classification. Compared with the single-scale semantic features of TPN, AMTIP can better preserve the local geometry between the support set and query set data via the adaptive multi-scale semantic fusion features. Extensive experiments demonstrate the superiority of the proposed AMTIP compared to the state-of-the-art few-shot classification models.

Joint feature representation and classification via adaptive graph semi-supervised nonnegative matrix factorization

As an effective feature representation method, non-negative matrix factorization (NMF) cannot utilize the label information sufficiently, which makes it not be suitable for the classification task. In this paper, we propose a joint feature representation and classification framework named adaptive graph semi-supervised nonnegative matrix factorization (AGSSNMF). Firstly, to enhance the discriminative ability of feature representation and accomplish the classification task, a regression model with nonnegative matrix factorization (called as RNMF) is proposed, which exploits the relation between the label information and feature representation. Secondly, to overcome the drawback of insufficient labels, an adaptive graph-based label propagation (refereed as AGLP) model is established, which adopts a local constraint to reflect the local structure of data. Then, we integrate RNMF and AGLP into a unified framework for feature representation and classification. Finally, an iterative optimization algorithm is used to solve the objective function. Extensive experiments show that the proposed framework has excellent performance compared with some well-known methods.

FUNMarker: Fusion Network-Based Method to Identify Prognostic and Heterogeneous Breast Cancer Biomarkers.

Breast cancer is a heterogeneous disease with many clinically distinguishable molecular subtypes each corresponding to a cluster of patients. Identification of prognostic and heterogeneous biomarkers for breast cancer is to detect cluster-specific gene biomarkers which can be used for accurate survival prediction of breast cancer outcomes. In this study, we proposed a FUsion Network-based method (FUNMarker) to identify prognostic and heterogeneous breast cancer biomarkers by considering the heterogeneity of patient samples and biological information from multiple sources. To reduce the affect of heterogeneity of patients, samples were first clustered using the K-means algorithm based on the principal components of gene expression. For each cluster, to comprehensively evaluate the influence of genes on breast cancer, genes were weighted from three aspects: biological function, prognostic ability and correlation with known disease genes. Then they were ranked via a label propagation model on a fusion network that combined physical protein interactions from seven types of networks and thus could reduce the impact of incompleteness of interactome. We compared FUNMarker with three state-of-the-art methods and the results showed that biomarkers identified by FUNMarker were biological interpretable and had stronger discriminative power than the existing methods in differentiating patients with different prognostic outcomes.

Adaptive Label Propagation for Facial Appearance Transfer

Facial appearance transfer (FAT) is a critical component of various facial editing tasks. It aims to transfer the facial appearance of a reference into a target with good visual consistency. When there are considerable visual differences between a reference and a target, however, it may introduce visual artifacts into the results. To tackle this problem, we propose a facial appearance map with illumination-aware and region-aware properties that allows seamless FAT. We formulate the appearance-map generation as label propagation (LP) on a similarity graph, and propose a new regularization structure to facilitate the adaptive appearance-map diffusion. Solving the original LP model of appearance map in general requires on the order $O(kn^2)$ time for an $n$ -nodes graph where each node has $k$ neighbors. It may be computationally prohibitive for an image with a large spatial resolution. To tackle this problem, we mathematically analyze the graph-based LP model and propose a fast algorithm with smart subset sampling. It selects a subset with $m$ nodes of the graph with $n$ nodes ( $m\ll n$ ) to approximate the solution to the original system, which significantly reduces its computational requirements from $O(kn^2)$ to $O(m^2n)$ . Based on the adaptive LP-based appearance map, we construct a framework to achieve various editing effects with FAT, including face replacement, face dubbing, face swapping, and transfiguring. Comparisons with related methods show the effectiveness of the adaptive LP model for FAT. Qualitative and quantitative evaluations verify the computational improvements of the approximation algorithm.

Label Propagation Ensemble for Hyperspectral Image Classification

The imbalance between limited labeled pixels and high dimensionality of hyperspectral data can easily give rise to Hughes phenomenon. Semisupervised learning (SSL) methods provide promising solutions to address the aforementioned issue. Graph-based SSL algorithms, also called label propagation methods, have obtained increasing attention in hyperspectral image (HSI) classification. However, the graphs constructed by utilizing the geometrical structure similarity of samples are unreliable due to the high dimensionality and complexity of the HSIs, especially for the case of very limited labeled pixels. Our motivation is to construct label propagation ensemble (LPE) model, then use the decision fusion of multiple label propagations to obtain pseudolabeled pixels with high classification confidence. In LPE, random subspace method is introduced to partition the feature space into multiple subspaces, then several label propagation models are constructed on corresponding subspaces, finally the results of different label propagation models are fused at decision level, and only the unlabeled pixels whose label propagation results are the same will be assigned with pseudolabels. Meanwhile extreme learning machine classifiers are trained on the labeled and pseudolabeled samples during the iteration. Compared with traditional label propagation methods, our proposed method can deal with the situation of very limited labeled samples by providing pseudolabeled pixels with high classification confidence, consequently, the accurate base classifiers are obtained. To demonstrate the effectiveness of the proposed method, LPE is compared with several state-of-the-art methods on four hyperspectral datasets. In addition, the method that only use label propagation is investigated to show the importance of ensemble technique in LPE. The experimental results demonstrate that the proposed method can provide competitive solution for HSI classification.

Label Propagation on K-partite Graphs with Heterophily

In this paper, for the first time, we study label propagation in heterogeneous graphs under heterophily assumption. Homophily label propagation (i.e., two connected nodes share similar labels) in homogeneous graph (with same types of vertices and relations) has been extensively studied before. Unfortunately, real-life networks (e.g., social networks) are heterogeneous, they contain different types of vertices (e.g., users, images, and texts) and relations (e.g., friendships and co-tagging) and allow for each node to propagate both the same and opposite copy of labels to its neighbors. We propose a $\mathcal {K}$ K -partite label propagation model to handle the mystifying combination of heterogeneous nodes/relations and heterophily propagation. With this model, we develop a novel label inference algorithm framework with update rules in near-linear time complexity. Since real networks change over time, we devise an incremental approach, which supports fast updates for both new data and evidence (e.g., ground truth labels) with guaranteed efficiency. We further provide a utility function to automatically determine whether an incremental or a re-modeling approach is favored. Extensive experiments on real datasets have verified the effectiveness and efficiency of our approach, and its superiority over the state-of-the-art label propagation methods.

Predicting microRNA-disease associations using label propagation based on linear neighborhood similarity

Interactions between microRNAs (miRNAs) and diseases can yield important information for uncovering novel prognostic markers. Since experimental determination of disease-miRNA associations is time-consuming and costly, attention has been given to designing efficient and robust computational techniques for identifying undiscovered interactions. In this study, we present a label propagation model with linear neighborhood similarity, called LPLNS, to predict unobserved miRNA-disease associations. Additionally, a preprocessing step is performed to derive new interaction likelihood profiles that will contribute to the prediction since new miRNAs and diseases lack known associations. Our results demonstrate that the LPLNS model based on the known disease-miRNA associations could achieve impressive performance with an AUC of 0.9034. Furthermore, we observed that the LPLNS model based on new interaction likelihood profiles could improve the performance to an AUC of 0.9127. This was better than other comparable methods. In addition, case studies also demonstrated our method’s outstanding performance for inferring undiscovered interactions between miRNAs and diseases, especially for novel diseases.

Directional Label Rectification in Adaptive Graph

With the explosive growth of multivariate time-series data, failure (event) analysis has gained widespread applications. A primary goal for failure analysis is to identify the fault signature, i.e., the unique feature pattern to distinguish failure events. However, the complex nature of multivariate time-series data brings challenge in the detection of fault signature. Given a time series from a failure event, the fault signature and the onset of failure are not necessarily adjacent, and the interval between the signature and failure is usually unknown. The uncertainty of such interval causes the uncertainty in labeling timestamps, thus makes it inapplicable to directly employ any standard supervised algorithms in signature detection. To address this problem, we present a novel directional label rectification model which identifies the fault-relevant timestamps and features in a simultaneous approach. Different from previous graph-based label propagation models using fixed graph, we propose to learn an adaptive graph which is optimal for the label rectification process. We conduct extensive experiments on both synthetic and real world datasets and illustrate the advantage of our model in both effectiveness and efficiency.

Saliency propagation with perceptual cues and background-excluded seeds

Graph-based methods have shown their potentialities for saliency detection. In this paper, a graph-based framework is proposed for saliency detection, which incorporates perceptual cues into the framework and uses the background-excluded seeds to propagate saliency. Firstly, a graph is constructed by two perceptual cues, including proximity and similarity. Secondly, probable background nodes are generated by a novel background probability measure and used to pick out reliable seeds. Then a label propagation model is developed to diffuse saliency based on these reliable seeds. Lastly, another perceptual cue called rareness is integrated into a cost function to optimize the propagation result. Results on four datasets demonstrate that the proposed method achieves superior performance against fifteen state-of-the-art methods in terms of different evaluation metrics.

Improved Label Propagation Model to Predict Drug - drug Interactions

Drug-drug interactions (DDIs) is one of the most concerned issues in drug design. Accurate prediction of potential DDIs in clinical trials can reduce the occurrence of side effects in real life of drugs. Therefore, we propose a model to predict DDIs. The model integrates several methods that can improve label propagation algorithm. Firstly, the chi-square test (CHI) method is adopted to filter or select the features that contain a large amount of information. Secondly, the sample similarity calculation method is reconstructed by label similarity and feature similarity. Then the label initialization information of unlabeled samples is constructed. Finally, we use label propagation algorithm to estimate the labels of the unlabeled drugs. The results show that the proposed model can obtain higher the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPR), which provides a favorable guarantee for the discovery of DDIs in the clinical stage.