BACKGROUND: Coronavirus disease 2019 (COVID-19) has spurred a boom in uncovering repurposable existing drugs. Drug repurposing is a strategy for identifying new uses for approved or investigational drugs that are outside the scope of the original medical indication. MOTIVATION: Current works of drug repurposing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are mostly limited to only focusing on chemical medicines, analysis of single drug targeting single SARS-CoV-2 protein, one-size-fits-all strategy using the same treatment (same drug) for different infected stages of SARS-CoV-2. To dilute these issues, we initially set the research focusing on herbal medicines. We then proposed a heterogeneous graph embedding method to signaled candidate repurposing herbs for each SARS-CoV-2 protein, and employed the variational graph convolutional network approach to recommend the precision herb combinations as the potential candidate treatments against the specific infected stage. METHOD: We initially employed the virtual screening method to construct the 'Herb-Compound' and 'Compound-Protein' docking graph based on 480 herbal medicines, 12,735 associated chemical compounds and 24 SARS-CoV-2 proteins. Sequentially, the 'Herb-Compound-Protein' heterogeneous network was constructed by means of the metapath-based embedding approach. We then proposed the heterogeneous-information-network-based graph embedding method to generate the candidate ranking lists of herbs that target structural, nonstructural and accessory SARS-CoV-2 proteins, individually. To obtain precision synthetic effective treatments forvarious COVID-19 infected stages, we employed the variational graph convolutional network method to generate candidate herb combinations as the recommended therapeutic therapies. RESULTS: There were 24 ranking lists, each containing top-10 herbs, targeting 24 SARS-CoV-2 proteins correspondingly, and 20 herb combinations were generated as the candidate-specific treatment to target the four infected stages. The code and supplementary materials are freely available at https://github.com/fanyang-AI/TCM-COVID19.

Discovering regularities in Traditional Chinese Medicine (TCM) formula has been a hot topic in assisting TCM clinical treatment and poly-pharmacology research. Several machine learning methods, like topic model, auto-encoder, and GNNs, have been proposed for discovering regularities in TCM. However, they are often limited by specific data challenges (e.g., complex relations with rich TCM knowledge, sparsity and ambiguity, expensive data labeling, etc.) in TCM formulae. Addressing these challenges, we first establish a TCM Attributed Heterogeneous Information Network (TAHIN) for modeling massive formulae, which can assemble various types of additional information and capture their relations. Based on the TAHIN, we further propose a novel hybrid-scales graph contrastive learning framework to learn high-quality node representations in a whole unsupervised manner which can be helpful for various tasks of discovering regularities such as herb classification and herb similarity search, etc. Extensive experiments demonstrate the effectiveness and interpretability of our method. Our source code and datasets are available at https://github.com/Yonggie/ HsCTRD.

Histopathological examination of tissue biopsies plays a fundamental role in disease assessment. Automatic histopathology image analysis requires substantial task-specific annotations, which are often expensive and laborious in realworld scenarios. This insufficient annotation of data limits the generalization ability of supervised learning models. To address this challenge, we propose a self-supervised Deformation Representation Learning (DRL) framework to learn semantic features from unlabeled data. As a novel paradigm, our approach utilizes deformation as supervisory signals based on two critical features, i.e., local structure heterogeneity and global context homogeneity. Given an original histopathology image and its deformed counterpart, there exists a moderate difference in local structures. In contrast, due to the transformation-invariance, both images share a similar global context compared with other images. Specifically, an encoder network is trained to distinguish the local inconsistency by measuring the mutual information and maintain the global consistency with noise contrastive estimation. Extensive experiments on public histopathology image datasets show that the learned representations are generalizable for various downstream tasks, such as transfer learning on segmentation and semi-supervised classification. Our approach achieves superior results over other self-supervised methods and the ImageNet pre-trained model, and it reveals the ability as a novel pre-training scheme in histopathology image analysis.

Absence epilepsy is one of the most common types of epilepsy. The diagnosis of absence epilepsy is among the greatest challenges faced by clinical neurologists due to a lack of easily observable symptoms that are present in conventional epilepsy (e.g. spasm and convulsion), and highly relies on the detection of Spike and Slow Waves (SSWs) in Electroencephalogram (EEG) signals. Recently, graph representations called complex networks have been increasingly applied to characterizing 1D EEG signals. However, existing methods often fail to effectively represent SSWs, struggling to capture the differences between SSW waveforms and their non-SSW counterparts, such as minute differences and distinct shapes. Addressing this issue, in this work, we propose two simple yet effective complex networks, Overlook Graph (OG) and Weighted Overlook Graph (WOG), which have been customized to expressively represent SSWs. Built upon OG and WOG, we then develop a 2D Convolutional Neural Network (2D-CNN) to further learn latent features from the graph representations and accomplish the detection task. Extensive experiments on a real-world absence epilepsy EEG dataset show that the proposed OG/WOG-2D-CNN method can accurately detect SSWs. Additional experiments on the well-known Bonn dataset further show that our method can generalize to the conventional epilepsy seizure detection task with highly competitive performances.

Bleeding in gastrointestinal tract (GI) is caused by many different diseases and may lead to serious consequence if not being treated correctly. The most effective method to locate bleeding area is using wireless capsule endoscopy (WCE). However, the amount of WCE images of one patient is so large that even a professional physician may take a long time on analyzing them, and human errors may also occur with work time increasing. Thus the computer-aided diagnosis methods become more attractive. In this paper, we propose a novel deep learning based method for WCE images bleeding area segmentation. We design multi-stage architecture and attention blocks to deal with small areas segmentation. The latter stages' input is the combination of feature maps transferred from former stages so that even very deep layers can obtain small areas features, and attention blocks help shallow layers to better extract small areas features thus they can transfer more useful information to latter stages. Extensive experiments are conducted on public available WCE image dataset to show the effect of multi-stage architecture and attention blocks. Compared with other bleeding areas segmentation methods, our approach achieves state-of-the-art performance with 98.5% overall accuracy, 90.1% mean accuracy and 86.3% mean intersection over union (IoU). There is a 10.7% improvement on mean IoU compared with the previous state-of-the-art WCE bleeding segmentation method.

Regularities analysis for prescriptions is a significant task for traditional Chinese medicine (TCM), both in inheritance of clinical experience and in improvement of clinical quality. Recently, many methods have been proposed for regularities discovery, but this task is challenging due to the quantity, sparsity and free-style of prescriptions. In this paper, we address the specific problem of regularities discovery and propose a graph embedding based framework for regularities discovery for massive prescriptions. We model this task as a relation prediction in which the correlation of two herbs or of herb and symptom are incorporated to characterize the different relationships. Specifically, we first establish a heterogeneous network with herbs and symptoms as its nodes. We develop a bipartite embedding model termed HS2Vec to detect regularities, which explores multiple relations of herbherb, and herb-symptom based on the heterogeneous network. Experiments on four real-world datasets demonstrate that the proposed framework is very effective for regularities discovery.

There has been a continuing demand for traditional and complementary medicine worldwide. A fundamental and important topic in Traditional Chinese Medicine (TCM) is to optimize the prescription and to detect herb regularities from TCM data. In this paper, we propose a novel clustering model to solve this general problem of herb categorization, a pivotal task of prescription optimization and herb regularities. The model utilizes Random Walks method, Bayesian rules and Expectation Maximization(EM) models to complete a clustering analysis effectively on a heterogeneous information network. We performed extensive experiments on the real-world datasets and compared our method with other algorithms and experts. Experimental results have demonstrated the effectiveness of the proposed model for discovering useful categorization of herbs and its potential clinical manifestations.