Multi-view attribute learning and context relationship encoding enhanced segmentation of lung tumors from CT images.

Graph convolutional neural networks (GCN) have shown the promise in medical image segmentation due to the flexibility of representing diverse range of image regions using graph nodes and propagating knowledge via graph edges. However, existing methods did not fully exploit the various attributes of image nodes and the context relationship among their attributes. We propose a new segmentation method with multi-similarity view enhancement and node attribute context learning (MNSeg). First, multiple views were formed by measuring the similarities among the image nodes, and MNSeg has a GCN based multi-view image node attribute learning (MAL) module to integrate various node attributes learnt from multiple similarity views. Each similarity view contains the specific similarities among all the image nodes, and it was integrated with the node attributes from all the channels to form the enhanced attributes of image nodes. Second, the context relationships among the attributes of image nodes are formulated by a transformer-based context relationship encoding (CRE) strategy to propagate these relationships across all the image nodes. During the transformer-based learning, the relationships were estimated based on the self-attention on all the image nodes, and then they were encoded into the learned node features. Finally, we design an attention at attribute category level (ACA) to discriminate and fuse the learnt diverse information from MAL, CRE, and the original node attributes. ACA identifies the more informative attribute categories by adaptively learn their importance. We validate the performance of MNSeg on a public lung tumor CT dataset and an in-house non-small cell lung cancer (NSCLC) dataset collected from the hospital. The segmentation results show that MNSeg outperformed the compared segmentation methods in terms of spatial overlap and the shape similarities. The ablation studies demonstrated the effectiveness of MAL, CRE, and ACA. The generalization ability of MNSeg was proved by the consistent improved segmentation performances using different 3D segmentation backbones.

Meta-Path Semantic and Global-Local Representation Learning Enhanced Graph Convolutional Model for Disease-Related miRNA Prediction.

Dysregulation of miRNAs is closely related to the progression of various diseases, so identifying disease-related miRNAs is crucial. Most recently proposed methods are based on graph reasoning, while they did not completely exploit the topological structure composed of the higher-order neighbor nodes and the global and local features of miRNA and disease nodes. We proposed a prediction method, MDAP, to learn semantic features of miRNA and disease nodes based on various meta-paths, as well as node features from the entire heterogeneous network perspective, and node pair attributes. Firstly, for both the miRNA and disease nodes, node category-wise meta-paths were constructed to integrate the similarity and association connection relationships. Each target node has its specific neighbor nodes for each meta-path, and the neighbors of longer meta-paths constitute its higher-order neighbor topological structure. Secondly, we constructed a meta-path specific graph convolutional network module to integrate the features of higher-order neighbors and their topology, and then learned the semantic representations of nodes. Thirdly, for the entire miRNA-disease heterogeneous network, a global-aware graph convolutional autoencoder was built to learn the network-view feature representations of nodes. We also designed semantic-level and representation-level attentions to obtain informative semantic features and node representations. Finally, the strategy based on the parallel convolutional-deconvolutional neural networks were designed to enhance the local feature learning for a pair of miRNA and disease nodes. The experiment results showed that MDAP outperformed other state-of-the-art methods, and the ablation experiments demonstrated the effectiveness of MDAP's major innovations. MDAP's ability in discovering potential disease-related miRNAs was further analyzed by the case studies over three diseases.

Graph reasoning method enhanced by relational transformers and knowledge distillation for drug-related side effect prediction.

Identifying the side effects related to drugs is beneficial for reducing the risk of drug development failure and saving the drug development cost. We proposed a graph reasoning method, RKDSP, to fuse the semantics of multiple connection relationships, the local knowledge within each meta-path, the global knowledge among multiple meta-paths, and the attributes of the drug and side effect node pairs. We constructed drug-side effect heterogeneous graphs consisting of the drugs, side effects, and their similarity and association connections. Multiple relational transformers were established to learn node features from diverse meta-path semantic perspectives. A knowledge distillation module was constructed to learn local and global knowledge of multiple meta-paths. Finally, an adaptive convolutional neural network-based strategy was presented to adaptively encode the attributes of each drug-side effect node pair. The experimental results demonstrated that RKDSP outperforms the compared state-of-the-art prediction approaches.

Usability evaluation of a glove-type wearable device for efficient biometric collection during triage.

To efficiently allocate medical resources at disaster sites, medical workers perform triage to prioritize medical treatments based on the severity of the wounded or sick. In such instances, evaluators often assess the severity status of the wounded or sick quickly, but their measurements are qualitative and rely on experience. Therefore, we developed a wearable device called Medic Hand in this study to extend the functionality of a medical worker's hand so as to measure multiple biometric indicators simultaneously without increasing the number of medical devices to be carried. Medic Hand was developed to quantitatively and efficiently evaluate "perfusion" during triage. Speed is essential during triage at disaster sites, where time and effort are often spared to attach medical devices to patients, so the use of Medic Hand as a biometric measurement device is more efficient for collecting biometric information. For Medic Hand to be handy during disasters, it is essential to understand and improve upon factors that facilitate its public acceptance. To this end, this paper reports on the usability evaluation of Medic Hand through a questionnaire survey of nonmedical workers.

Effects of Sitting and Supine Positions on Tongue Color as Measured by Tongue Image Analyzing System and Its Relation to Biometric Information.

Tongue diagnosis is one of the important diagnostic methods in Kampo (traditional Japanese) medicine, in which the color and shape of the tongue are used to determine the patient's constitution and systemic symptoms. Tongue diagnosis is performed with the patient in the sitting or supine positions; however, the differences in tongue color in these two different positions have not been analyzed. We developed tongue image analyzing system (TIAS), which can quantify tongue color by capturing tongue images in the sitting and supine positions. We analyzed the effects on tongue color in two different body positions. Tongue color was quantified as L∗a∗b∗ from tongue images of 18 patients in two different body positions by taking images with TIAS. The CIEDE 2000 color difference equation (ΔE00) was used to assess the difference in tongue color in two different body positions. Correlations were also determined between ΔE00, physical characteristics, and laboratory test values. The mean and median ΔE00 for 18 patients were 2.85 and 2.34, respectively. Of these patients, 77.8% had a ΔE00 < 4.1. A weak positive correlation was obtained between ΔE00 and systolic blood pressure and fasting plasma glucose. Approximately 80% of patients' tongue color did not change between the sitting and supine positions. This indicates that the diagnostic results of tongue color are trustworthy even if medical professionals perform tongue diagnosis in two different body positions.

Breath Measurement Method for Synchronized Reproduction of Biological Tones in an Augmented Reality Auscultation Training System.

An educational augmented reality auscultation system (EARS) is proposed to enhance the reality of auscultation training using a simulated patient. The conventional EARS cannot accurately reproduce breath sounds according to the breathing of a simulated patient because the system instructs the breathing rhythm. In this study, we propose breath measurement methods that can be integrated into the chest piece of a stethoscope. We investigate methods using the thoracic variations and frequency characteristics of breath sounds. An accelerometer, a magnetic sensor, a gyro sensor, a pressure sensor, and a microphone were selected as the sensors. For measurement with the magnetic sensor, we proposed a method by detecting the breathing waveform in terms of changes in the magnetic field accompanying the surface deformation of the stethoscope based on thoracic variations using a magnet. During breath sound measurement, the frequency spectra of the breath sounds acquired by the built-in microphone were calculated. The breathing waveforms were obtained from the difference in characteristics between the breath sounds during exhalation and inhalation. The result showed the average value of the correlation coefficient with the reference value reached 0.45, indicating the effectiveness of this method as a breath measurement method. And the evaluations suggest more accurate breathing waveforms can be obtained by selecting the measurement method according to breathing method and measurement point.

Learning Association Characteristics by Dynamic Hypergraph and Gated Convolution Enhanced Pairwise Attributes for Prediction of Disease-Related lncRNAs.

As the long non-coding RNAs (lncRNAs) play important roles during the incurrence and development of various human diseases, identifying disease-related lncRNAs can contribute to clarifying the pathogenesis of diseases. Most of the recent lncRNA-disease association prediction methods utilized the multi-source data about the lncRNAs and diseases. A single lncRNA may participate in multiple disease processes, and multiple lncRNAs usually are involved in the same disease process synergistically. However, the previous methods did not completely exploit the biological characteristics to construct the informative prediction models. We construct a prediction model based on adaptive hypergraph and gated convolution for lncRNA-disease association prediction (AGLDA), to embed and encode the biological characteristics about lncRNA-disease associations, the topological features from the entire heterogeneous graph perspective, and the gated enhanced pairwise features. First, the strategy for constructing hyperedges is designed to reflect the biological characteristic that multiple lncRNAs are involved in multiple disease processes. Furthermore, each hyperedge has its own biological perspective, and multiple hyperedges are beneficial for revealing the diverse relationships among multiple lncRNAs and diseases. Second, we encode the biological features of each lncRNA (disease) node using a strategy based on dynamic hypergraph convolutional networks. The strategy may adaptively learn the features of the hyperedges and formulate the dynamically evolved hypergraph topological structure. Third, a group convolutional network is established to integrate the entire heterogeneous topological structure and multiple types of node attributes within an lncRNA-disease-miRNA graph. Finally, a gated convolutional strategy is proposed to enhance the informative features of the lncRNA-disease node pairs. The comparison experiments indicate that AGLDA outperforms seven advanced prediction methods. The ablation studies confirm the effectiveness of major innovations, and the case studies validate AGLDA's ability in application for discovering potential disease-related lncRNA candidates.

Complementary feature learning across multiple heterogeneous networks and multimodal attribute learning for predicting disease-related miRNAs.

Inferring the latent disease-related miRNAs is helpful for providing a deep insight into observing the disease pathogenesis. We propose a method, CMMDA, to encode and integrate the context relationship among multiple heterogeneous networks, the complementary information across these networks, and the pairwise multimodal attributes. We first established multiple heterogeneous networks according to the diverse disease similarities. The feature representation embedding the context relationship is formulated for each miRNA (disease) node based on transformer. We designed a co-attention fusion mechanism to encode the complementary information among multiple networks. In terms of a pair of miRNA and disease nodes, the pairwise attributes from multiple networks form a multimodal attribute embedding. A module based on depthwise separable convolution is constructed to enhance the encoding of the specific features from each modality. The experimental results and the ablation studies show that CMMDA's superior performance and the effectiveness of its major innovations.

Mutually enhanced multi-view information learning for segmentation of lung tumor in CT images.

Objective.The accurate automatic segmentation of tumors from computed tomography (CT) volumes facilitates early diagnosis and treatment of patients. A significant challenge in tumor segmentation is the integration of the spatial correlations among multiple parts of a CT volume and the context relationship across multiple channels.Approach.We proposed a mutually enhanced multi-view information model (MEMI) to propagate and fuse the spatial correlations and the context relationship and then apply it to lung tumor CT segmentation. First, a feature map was obtained from segmentation backbone encoder, which contained many image region nodes. An attention mechanism from the region node perspective was presented to determine the impact of all the other nodes on a specific node and enhance the node attribute embedding. A gated convolution-based strategy was also designed to integrate the enhanced attributes and the original node features. Second, transformer across multiple channels was constructed to integrate the channel context relationship. Finally, since the encoded node attributes from the gated convolution view and those from the channel transformer view were complementary, an interaction attention mechanism was proposed to propagate the mutual information among the multiple views.Main results.The segmentation performance was evaluated on both public lung tumor dataset and private dataset collected from a hospital. The experimental results demonstrated that MEMI was superior to other compared segmentation methods. Ablation studies showed the contributions of node correlation learning, channel context relationship learning, and mutual information interaction across multiple views to the improved segmentation performance. Utilizing MEMI on multiple segmentation backbones also demonstrated MEMI's generalization ability.Significance.Our model improved the lung tumor segmentation performance by learning the correlations among multiple region nodes, integrating the channel context relationship, and mutual information enhancement from multiple views.

Comparative analysis of alignment algorithms for macular optical coherence tomography imaging.

BACKGROUND: Optical coherence tomography (OCT) is the most important and commonly utilized imaging modality in ophthalmology and is especially crucial for the diagnosis and management of macular diseases. Each OCT volume is typically only available as a series of cross-sectional images (B-scans) that are accessible through proprietary software programs which accompany the OCT machines. To maximize the potential of OCT imaging for machine learning purposes, each OCT image should be analyzed en bloc as a 3D volume, which requires aligning all the cross-sectional images within a particular volume. METHODS: A dataset of OCT B-scans obtained from 48 age-related macular degeneration (AMD) patients and 50 normal controls was used to evaluate five registration algorithms. After alignment of B-scans from each patient, an en face surface map was created to measure the registration quality, based on an automatically generated Laplace difference of the surface map-the smoother the surface map, the smaller the average Laplace difference. To demonstrate the usefulness of B-scan alignment, we trained a 3D convolutional neural network (CNN) to detect age-related macular degeneration (AMD) on OCT images and compared the performance of the model with and without B-scan alignment. RESULTS: The mean Laplace difference of the surface map before registration was 27 ± 4.2 pixels for the AMD group and 26.6 ± 4 pixels for the control group. After alignment, the smoothness of the surface map was improved, with a mean Laplace difference of 5.5 ± 2.7 pixels for Advanced Normalization Tools Symmetric image Normalization (ANTs-SyN) registration algorithm in the AMD group and a mean Laplace difference of 4.3 ± 1.4.2 pixels for ANTs in the control group. Our 3D CNN achieved superior performance in detecting AMD, when aligned OCT B-scans were used (AUC 0.95 aligned vs. 0.89 unaligned). CONCLUSIONS: We introduced a novel metric to quantify OCT B-scan alignment and compared the effectiveness of five alignment algorithms. We confirmed that alignment could be improved in a statistically significant manner with readily available alignment algorithms that are available to the public, and the ANTs algorithm provided the most robust performance overall. We further demonstrated that alignment of OCT B-scans will likely be useful for training 3D CNN models.

Graph Reasoning Method Based on Affinity Identification and Representation Decoupling for Predicting lncRNA-Disease Associations.

An increasing number of studies have shown that dysregulation of lncRNAs is related to the occurrence of various diseases. Most of the previous methods, however, are designed based on homogeneity assumption that the representation of a target lncRNA (or disease) node should be updated by aggregating the attributes of its neighbor nodes. However, the assumption ignores the affinity nodes that are far from the target node. We present a novel prediction method, GAIRD, to fully leverage the heterogeneous information in the network and the decoupled node features. The first major innovation is a random walk strategy based on width-first searching and depth-first searching. Different from previous methods that only focus on homogeneous information, our new strategy learns both the homogeneous information within local neighborhoods and the heterogeneous information within higher-order neighborhoods. The second innovation is a representation decoupling module to extract the purer attributes and the purer topologies. Third, a module based on group convolution and deep separable convolution is developed to promote the pairwise intrachannel and interchannel feature learning. The experimental results show that GAIRD outperforms comparing state-of-the-art methods, and the ablation studies prove the contributions of major innovations. We also performed case studies on 3 diseases to further demonstrate the effectiveness of the GAIRD model in applications.

Graph generative and adversarial strategy-enhanced node feature learning and self-calibrated pairwise attribute encoding for prediction of drug-related side effects.

Background: Inferring drug-related side effects is beneficial for reducing drug development cost and time. Current computational prediction methods have concentrated on graph reasoning over heterogeneous graphs comprising the drug and side effect nodes. However, the various topologies and node attributes within multiple drug-side effect heterogeneous graphs have not been completely exploited. Methods: We proposed a new drug-side effect association prediction method, GGSC, to deeply integrate the diverse topologies and attributes from multiple heterogeneous graphs and the self-calibration attributes of each drug-side effect node pair. First, we created two heterogeneous graphs comprising the drug and side effect nodes and their related similarity and association connections. Since each heterogeneous graph has its specific topology and node attributes, a node feature learning strategy was designed and the learning for each graph was enhanced from a graph generative and adversarial perspective. We constructed a generator based on a graph convolutional autoencoder to encode the topological structure and node attributes from the whole heterogeneous graph and then generate the node features embedding the graph topology. A discriminator based on multilayer perceptron was designed to distinguish the generated topological features from the original ones. We also designed representation-level attention to discriminate the contributions of topological representations from multiple heterogeneous graphs and adaptively fused them. Finally, we constructed a self-calibration module based on convolutional neural networks to guide pairwise attribute learning through the features of the small latent space. Results: The comparison experiment results showed that GGSC had higher prediction performance than several state-of-the-art prediction methods. The ablation experiments demonstrated the effectiveness of topological enhancement learning, representation-level attention, and self-calibrated pairwise attribute learning. In addition, case studies over five drugs demonstrated GGSC's ability in discovering the potential drug-related side effect candidates. Conclusion: We proposed a drug-side effect association prediction method, and the method is beneficial for screening the reliable association candidates for the biologists to discover the actual associations.

Learning Multi-Types of Neighbor Node Attributes and Semantics by Heterogeneous Graph Transformer and Multi-View Attention for Drug-Related Side-Effect Prediction.

Since side-effects of drugs are one of the primary reasons for their failure in clinical trials, predicting their side-effects can help reduce drug development costs. We proposed a method based on heterogeneous graph transformer and capsule networks for side-effect-drug-association prediction (TCSD). The method encodes and integrates attributes from multiple types of neighbor nodes, connection semantics, and multi-view pairwise information. In each drug-side-effect heterogeneous graph, a target node has two types of neighbor nodes, the drug nodes and the side-effect ones. We proposed a new heterogeneous graph transformer-based context representation learning module. The module is able to encode specific topology and the contextual relations among multiple kinds of nodes. There are similarity and association connections between the target node and its various types of neighbor nodes, and these connections imply semantic diversity. Therefore, we designed a new strategy to measure the importance of a neighboring node to the target node and incorporate different semantics of the connections between the target node and its multi-type neighbors. Furthermore, we designed attentions at the neighbor node type level and at the graph level, respectively, to obtain enhanced informative neighbor node features and multi-graph features. Finally, a pairwise multi-view feature learning module based on capsule networks was built to learn the pairwise attributes from the heterogeneous graphs. Our prediction model was evaluated using a public dataset, and the cross-validation results showed it achieved superior performance to several state-of-the-art methods. Ablation experiments undertaken demonstrated the effectiveness of heterogeneous graph transformer-based context encoding, the position enhanced pairwise attribute learning, and the neighborhood node category-level attention. Case studies on five drugs further showed TCSD's ability in retrieving potential drug-related side-effect candidates, and TCSD inferred the candidate side-effects for 708 drugs.

Specific topology and topological connection sensitivity enhanced graph learning for lncRNA-disease association prediction.

Predicting disease-related candidate long noncoding RNAs (lncRNAs) is beneficial for exploring disease pathogenesis due to the close relations between lncRNAs and the occurrence and development of human diseases. It is a long-term and challenging task to adequately extract specific and local topologies in individual lncRNA network and individual disease network, and integrate the information of the connection relationships. We propose a new graph learning-based prediction method to encode specific and local topologies from each individual network, neighbor topologies with different connection relationships, and pairwise attributes. We first construct a lncRNA network composed of all the lncRNA nodes and their similarities, and a single disease network that contains all the disease nodes and disease similarities. Then, a network-aware graph convolutional autoencoder is constructed to encode the specific and local topologies of each network. Secondly, a heterogeneous network is established to embed all lncRNA, disease, and miRNA nodes and their various connections. Afterwards, a connection-sensitive graph neural network is designed to deeply integrate the neighbor node attributes and connection characteristics in the heterogeneous network and learn neighbor topological representations. We also construct both connection-level and topology representation-level attention mechanisms to extract informative connections and topological representations. Finally, we build a multi-layer convolutional neural networks with weighted residuals to adaptively complement the detailed features to pairwise attribute encoding. Comprehensive experiments and comparison results demonstrated that NCPred outperforms seven state-of-the-art prediction methods. The ablation studies demonstrated the importance of local topology learning, neighbor topology learning, and pairwise attribute encoding. Case studies on prostate, lung, and breast cancers further revealed NCPred's capacity to screen potential candidate disease-related lncRNAs.

Cut-Off Value of Capillary Refill Time for Peripheral Circulatory Failure Diagnosis.

INTRODUCTION: Capillary refill time (CRT) is an indicator of peripheral circulation and is recommended in the 2021 guidelines for treating and managing sepsis. STUDY OBJECTIVE: This study developed a portable device to realize objective CRT measurement. Assuming that peripheral blood flow obstruction by the artery occlusion test (AOT) or venous occlusion test (VOT) increases the CRT, the cut-off value for peripheral circulatory failure was studied by performing a comparative analysis with CRT with no occlusion test (No OT). METHODS: Fourteen (14) healthy adults (age: 20-26 years) participated in the study. For the vascular occlusion test, a sphygmomanometer was placed on the left upper arm of the participant in the supine position, and a pressure of 30mmHg higher than the systolic pressure was applied for AOT, a pressure of 60mmHg was applied for VOT, respectively, and no pressure was applied for No OT. The CRT was measured from the index finger of the participant's left hand. RESULTS: Experimental results revealed that CRT was significantly longer in the AOT and did not differ significantly in the VOT. The cut-off value for peripheral circulatory failure was found to be 2.88 seconds based on Youden's index by using receiver operating characteristic (ROC) analysis with AOT as positive and No OT as negative. CONCLUSION: Significant results were obtained in a previous study on the evaluation of septic shock patients when CRT > three seconds was considered abnormal, and the cut-off value for peripheral circulatory failure in the current study validated this.

Topological structure and global features enhanced graph reasoning model for non-small cell lung cancer segmentation from CT.

Objective.Accurate and automated segmentation of lung tumors from computed tomography (CT) images is critical yet challenging. Lung tumors are of various sizes and locations and have indistinct boundaries adjacent to other normal tissues.Approach.We propose a new segmentation model that can integrate the topological structure and global features of image region nodes to address the challenges. Firstly, we construct a weighted graph with image region nodes. The graph topology reflects the complex spatial relationships among these nodes, and each node has its specific attributes. Secondly, we propose a node-wise topological feature learning module based on a new graph convolutional autoencoder (GCA). Meanwhile, a node information supplementation (GNIS) module is established by integrating specific features of each node extracted by a convolutional neural network (CNN) into each encoding layer of GCA. Afterwards, we construct a global feature extraction model based on multi-layer perceptron (MLP) to encode the features learnt from all the image region nodes which are crucial complementary information for tumor segmentation.Main results.Ablation study results over the public lung tumor segmentation dataset demonstrate the contributions of our major technical innovations. Compared with other segmentation methods, our new model improves the segmentation performance and has generalization ability on different 3D image segmentation backbones. Our model achieved Dice of 0.7827, IoU of 0.6981, and HD of 32.1743 mm on the public dataset 2018 Medical Segmentation Decathlon challenge, and Dice of 0.7004, IoU of 0.5704 and HD of 64.4661 mm on lung tumor dataset from Shandong Cancer Hospital.Significance. The novel model improves automated lung tumor segmentation performance especially the challenging and complex cases using topological structure and global features of image region nodes. It is of great potential to apply the model to other CT segmentation tasks.

Reliability of non-contact tongue diagnosis for Sjögren's syndrome using machine learning method.

Sjögren's syndrome (SS) is an autoimmune disease characterized by dry mouth. The cause of SS is unknown, and its diverse symptoms make diagnosis difficult. The Saxon test, an intraoral examination, is used as the primary diagnostic method for SS, however, the risk of salivary infection is problematic. Therefore, we investigate the possibility of diagnosing SS by non-contact and imaging observation of the tongue surface. In this study, we obtained tongue photographs of 60 patients at the Tsurumi University School of Dentistry outpatient clinic to clarify the relationship between the features of the tongue and SS. We divided the tongue into four regions, and the color of each region was transformed into CIE1976L*a*b* space and statistically analyzed. To clarify experimentally the possibility of SS diagnosis using tongue color, we employed three machine-learning models: logistic regression, support vector machine, and random forest. In addition, we constructed diagnostic prediction models based on the Bagging and Stacking methods combined with three machine-learning models for comparative evaluation. This analysis used dimensionality compression by principal component analysis to eliminate redundancy in tongue color information. We found a significant difference between the a* value of the rear part of the tongue and the b* value of the middle part of the tongue in SS and non-SS patients. In addition to the principal component scores of tongue color, the support vector machine was trained using age, and achieved high accuracy (71.3%) and specificity (78.1%). The results indicate that the prediction of SS diagnosis by tongue color reaches a level comparable to machine learning models trained using the Saxon test. This is the first study using machine learning to predict SS diagnosis by non-contact tongue observation. Our proposed method can potentially support early SS detection simply and conveniently, eliminating the risk of infection at diagnosis, and it should be validated and optimized in clinical practice.

Semantic Meta-Path Enhanced Global and Local Topology Learning for lncRNA-Disease Association Prediction.

Since abnormal expression of long non-coding RNAs (lncRNAs) is associated with various human diseases, identifying disease-related lncRNAs helps reveal the pathogenesis of diseases. Existing methods for lncRNA-disease association prediction mainly focus on multi-sourced data related to lncRNAs and diseases. The rich semantic information of meta-paths, composed of multiple kinds of connections between lncRNA and disease nodes, is neglected. We propose a new prediction method, MGLDA, to encode and integrate the semantics of multiple meta-paths, the global topology of heterogeneous graph, and pairwise attributes of lncRNA and disease nodes. First, a tri-layer heterogeneous graph is constructed to associate multi-sourced data across the lncRNA, disease, and miRNA nodes. Afterwards, we establish multiple meta-paths connecting the lncRNA and disease nodes to derive and denote various semantics. Each meta-path contains its specific semantics formulated by an embedding strategy, and each embedding covers local topology formed by the diverse semantic connections among the lncRNA, disease, and miRNA nodes. We construct multiple graph convolutional autoencoders (GCA) with topology-level attention to learn global and multiple local topologies from the tri-layer graph and each meta-path, respectively. The topology-level attention mechanism can learn the importance of various global and local topologies for adaptive pairwise topology fusion. Finally, a convolutional autoencoder learns the attribute representations of lncRNA-disease pairs, which integrates the learnt detailed and representative pairwise features. Experimental results show that MGLDA outperforms other state-of-the-art prediction methods in comparison and retrieves more real lncRNA-disease associations in the top-ranked candidates. The ablation study also demonstrates the important contributions of the local and global topology learning, and pairwise attribute learning. Case studies on three diseases further demonstrate MGLDA's ability to identify potential disease-related lncRNAs.

Convolutional bi-directional learning and spatial enhanced attentions for lung tumor segmentation.

BACKGROUND AND OBJECTIVE: Accurate lung tumor segmentation from computed tomography (CT) is complex due to variations in tumor sizes, shapes, patterns and growing locations. Learning semantic and spatial relations between different feature channels, image regions and positions is critical yet challenging. METHODS: We propose a new segmentation method, PRCS, by learning and integrating multi-channel contextual relations, and spatial and position dependencies across image regions. Firstly, to extract contextual relationships between different deep image feature tensor channels, we propose a new convolutional bi-directional gated recurrent unit based module for forward and backward learning. Secondly, a novel cross-channel region-level attention mechanism is proposed to discriminate the contributions of different local regions and associated features in the global learning process. Finally, spatial and position dependencies are formulated by a new position-enhanced self-attention mechanism. The new attention can measure the diverse contributions of other positions to a target position and obtain an enhanced adaptive feature vector for the target position. RESULTS: Our model outperformed seven state-of-the-art segmentation methods on both public and in-house lung tumor datasets in terms of spatial overlapping and shape similarity. Ablation study results proved the effectiveness of three technical innovations and generalization ability on different 3D CNN segmentation backbones. CONCLUSION: The proposed model enhanced the learning and propagation of contextual, spatial and position relations in 3D volumes, improving lung tumours' segmentation performance with large variations and indistinct boundaries. PRCS provides an effective automated approach to support precision diagnosis and treatment planning of lung cancer.