PMiSLocMF: predicting miRNA subcellular localizations by incorporating multi-source features of miRNAs.

The microRNAs (miRNAs) play crucial roles in several biological processes. It is essential for a deeper insight into their functions and mechanisms by detecting their subcellular localizations. The traditional methods for determining miRNAs subcellular localizations are expensive. The computational methods are alternative ways to quickly predict miRNAs subcellular localizations. Although several computational methods have been proposed in this regard, the incomplete representations of miRNAs in these methods left the room for improvement. In this study, a novel computational method for predicting miRNA subcellular localizations, named PMiSLocMF, was developed. As lots of miRNAs have multiple subcellular localizations, this method was a multi-label classifier. Several properties of miRNA, such as miRNA sequences, miRNA functional similarity, miRNA-disease, miRNA-drug, and miRNA-mRNA associations were adopted for generating informative miRNA features. To this end, powerful algorithms [node2vec and graph attention auto-encoder (GATE)] and one newly designed scheme were adopted to process above properties, producing five feature types. All features were poured into self-attention and fully connected layers to make predictions. The cross-validation results indicated the high performance of PMiSLocMF with accuracy higher than 0.83, average area under the receiver operating characteristic curve (AUC) and area under the precision-recall curve (AUPR) exceeding 0.90 and 0.77, respectively. Such performance was better than all previous methods based on the same dataset. Further tests proved that using all feature types can improve the performance of PMiSLocMF, and GATE and self-attention layer can help enhance the performance. Finally, we deeply analyzed the influence of miRNA associations with diseases, drugs, and mRNAs on PMiSLocMF. The dataset and codes are available at https://github.com/Gu20201017/PMiSLocMF.

Predicting microbe-drug associations with structure-enhanced contrastive learning and self-paced negative sampling strategy.

MOTIVATION: Predicting the associations between human microbes and drugs (MDAs) is one critical step in drug development and precision medicine areas. Since discovering these associations through wet experiments is time-consuming and labor-intensive, computational methods have already been an effective way to tackle this problem. Recently, graph contrastive learning (GCL) approaches have shown great advantages in learning the embeddings of nodes from heterogeneous biological graphs (HBGs). However, most GCL-based approaches don't fully capture the rich structure information in HBGs. Besides, fewer MDA prediction methods could screen out the most informative negative samples for effectively training the classifier. Therefore, it still needs to improve the accuracy of MDA predictions. RESULTS: In this study, we propose a novel approach that employs the Structure-enhanced Contrastive learning and Self-paced negative sampling strategy for Microbe-Drug Association predictions (SCSMDA). Firstly, SCSMDA constructs the similarity networks of microbes and drugs, as well as their different meta-path-induced networks. Then SCSMDA employs the representations of microbes and drugs learned from meta-path-induced networks to enhance their embeddings learned from the similarity networks by the contrastive learning strategy. After that, we adopt the self-paced negative sampling strategy to select the most informative negative samples to train the MLP classifier. Lastly, SCSMDA predicts the potential microbe-drug associations with the trained MLP classifier. The embeddings of microbes and drugs learning from the similarity networks are enhanced with the contrastive learning strategy, which could obtain their discriminative representations. Extensive results on three public datasets indicate that SCSMDA significantly outperforms other baseline methods on the MDA prediction task. Case studies for two common drugs could further demonstrate the effectiveness of SCSMDA in finding novel MDA associations. AVAILABILITY: The source code is publicly available on GitHub https://github.com/Yue-Yuu/SCSMDA-master.

DMGL-MDA: A dual-modal graph learning method for microbe-drug association prediction.

The interaction between human microbes and drugs can significantly impact human physiological functions. It is crucial to identify potential microbe-drug associations (MDAs) before drug administration. However, conventional biological experiments to predict MDAs are plagued by drawbacks such as time-consuming, high costs, and potential risks. On the contrary, computational approaches can speed up the screening of MDAs at a low cost. Most computational models usually use a drug similarity matrix as the initial feature representation of drugs and stack the graph neural network layers to extract the features of network nodes. However, different calculation methods result in distinct similarity matrices, and message passing in graph neural networks (GNNs) induces phenomena of over-smoothing and over-squashing, thereby impacting the performance of the model. To address these issues, we proposed a novel graph representation learning model, dual-modal graph learning for microbe-drug association prediction (DMGL-MDA). It comprises a dual-modal embedding module, a bipartite graph network embedding module, and a predictor module. To assess the performance of DMGL-MDA, we compared it against state-of-the-art methods using two benchmark datasets. Through cross-validation, we illustrated the superiority of DMGL-MDA. Furthermore, we conducted ablation experiments and case studies to validate the effective performance of the model.

DAS-DDI: A dual-view framework with drug association and drug structure for drug-drug interaction prediction.

In drug development and clinical application, drug-drug interaction (DDI) prediction is crucial for patient safety and therapeutic efficacy. However, traditional methods for DDI prediction often overlook the structural features of drugs and the complex interrelationships between them, which affect the accuracy and interpretability of the model. In this paper, a novel dual-view DDI prediction framework, DAS-DDI is proposed. Firstly, a drug association network is constructed based on similarity information among drugs, which could provide rich context information for DDI prediction. Subsequently, a novel drug substructure extraction method is proposed, which could update the features of nodes and chemical bonds simultaneously, improving the comprehensiveness of the feature. Furthermore, an attention mechanism is employed to fuse multiple drug embeddings from different views dynamically, enhancing the discriminative ability of the model in handling multi-view data. Comparative experiments on three public datasets demonstrate the superiority of DAS-DDI compared with other state-of-the-art models under two scenarios.

Could combination chemotherapy be more effective than monotherapy in the treatment of visceral leishmaniasis? A systematic review of preclinical evidence.

From a systematic review framework, we assessed the preclinical evidence on the effectiveness of drug combinations for visceral leishmaniasis (VL) treatment. Research protocol was based on the PRISMA guideline. Research records were identified from Medline, Scopus and Web of Science. Animal models, infection and treatment protocols, parasitological and immunological outcomes were analysed. The SYRCLE's (SYstematic Review Center for Laboratory Animal Experimentation) toll was used to evaluate the risk of bias in all studies reviewed. Fourteen papers using mice, hamster and dogs were identified. Leishmania donovani was frequently used to induce VL, which was treated with 23 drugs in 40 different combinations. Most combinations allowed to reduce the effective dose, cost and time of treatment, in addition to improving the parasitological control of Leishmania spp. The benefits achieved from drug combinations were associated with an increased drug's half-life, direct parasitic toxicity and improved immune defences in infected hosts. Selection, performance and detection bias were the main limitations identified. Current evidence indicates that combination chemotherapy, especially those based on classical drugs (miltefosine, amphotericin B antimony-based compounds) and new drugs (CAL-101, PAM3Cys, tufisin and DB766), develops additive or synergistic interactions, which trigger trypanocidal and immunomodulatory effects associated with reduced parasite load, organ damage and better cure rates in VL.

It is time to apply biclustering: a comprehensive review of biclustering applications in biological and biomedical data.

Biclustering is a powerful data mining technique that allows clustering of rows and columns, simultaneously, in a matrix-format data set. It was first applied to gene expression data in 2000, aiming to identify co-expressed genes under a subset of all the conditions/samples. During the past 17 years, tens of biclustering algorithms and tools have been developed to enhance the ability to make sense out of large data sets generated in the wake of high-throughput omics technologies. These algorithms and tools have been applied to a wide variety of data types, including but not limited to, genomes, transcriptomes, exomes, epigenomes, phenomes and pharmacogenomes. However, there is still a considerable gap between biclustering methodology development and comprehensive data interpretation, mainly because of the lack of knowledge for the selection of appropriate biclustering tools and further supporting computational techniques in specific studies. Here, we first deliver a brief introduction to the existing biclustering algorithms and tools in public domain, and then systematically summarize the basic applications of biclustering for biological data and more advanced applications of biclustering for biomedical data. This review will assist researchers to effectively analyze their big data and generate valuable biological knowledge and novel insights with higher efficiency.

Evaluation of the anti-Trypanosoma cruzi activity in vitro and in vivo of silibinin and silibinin in association to benznidazole.

Chagas disease (CD) is endemic in Latin America. Drugs available for its treatment are benznidazole (BZ)/nifurtimox (NF), both with low efficacy in the late infection and responsible for several side effects. Studies of new drugs for CD among natural products, and using drug combinations with BZ/NF are recommended. Silibinin (SLB) is a natural compound that inhibits the efflux pump (Pgp) of drugs in host cell membranes, causes death of trypanosomatids, has anti-inflammatory activity, and was never assayed against T. cruzi. Here, in vitro and in vivo activities of SLB, SLB+BZ, and BZ against T. cruzi Y strain were evaluated. Cytotoxicity of SLB in VERO cells by the MTT method revealed IC50 of 250.22 µM. The trypanocidal activity evaluated by resazurin method in epimastigotes showed that SLB 25 µM inhibited parasite growth. SLB IC50 and selectivity index (SI) for amastigote were 79.81 µM and 3.13, respectively. SLB100+BZ10 showed higher parasite inhibition (91.44%) than SLB or BZ. Swiss mice infected with Y strain were treated with SLB, SLB+BZ, and BZ. Parasitemia was evaluated daily and 90, 180, and 240 days after treatment in surviving animals by hemoculture, blood qPCR, and after euthanasia, by qPCR in heart tissue. SLB monotherapy was not able to control the parasitemia/mortality of the animals. Parasitological negativation of 85.7-100% was observed in the experimental groups treated with SLB+BZ. Although SLB had shown activity against T. cruzi in vitro, it was not active in mice. Thus, the results of the therapeutic effect observed with SLB+BZ may be interpreted as a result from BZ action.

Gallic and Ellagic Acids Are Promising Adjuvants to Conventional Amphotericin B for the Treatment of Cutaneous Leishmaniasis.

In this study, we demonstrated the potential associative effect of combining conventional amphotericin B (Amph B) with gallic acid (GA) and with ellagic acid (EA) in topical formulations for the treatment of cutaneous leishmaniasis in BALB/c mice. Preliminary stability tests of the formulations and in vitro drug release studies with Amph B, GA, Amph B plus GA, EA, and Amph B plus EA were carried out, as well as assessment of the in vivo treatment of BALB/c mice infected with Leishmania major After 40 days of infection, the animals were divided into 6 groups and treated twice a day for 21 days with a gel containing Amph B, GA, Amph B plus GA, EA, or Amph B plus EA, and the negative-control group was treated with the vehicle. In the animals that received treatment, there was reduction of the lesion size and reduction of the parasitic load. Histopathological analysis of the treatments with GA, EA, and combinations with Amph B showed circumscribed lesions with the presence of fibroblasts, granulation tissue, and collagen deposition, as well as the presence of activated macrophages. The formulations containing GA and EA activated macrophages in all evaluated parameters, resulting in the activation of cells of the innate immune response, which can generate healing and protection. GA and EA produced an associative effect with Amph B, which makes them promising for use with conventional Amph B in the treatment of cutaneous leishmaniasis.

Therapies in preclinical and in early clinical development for the treatment of urinary tract infections: from pathogens to therapies.

INTRODUCTION: Urinary tract infections (UTIs) are a prevalent health challenge characterized by the invasion and multiplication of microorganisms in the urinary system. The continuous exploration of novel therapeutic interventions is imperative. Advances in research offer hope for revolutionizing the management of UTIs and improving the overall health outcomes for individuals affected by these infections. AREAS COVERED: This review aimed to provide an overview of existing treatments for UTIs, highlighting their strengths and limitations. Moreover, we explored and analyzed the latest therapeutic modalities under clinical development. Finally, the review offered a picture into the potential implications of these therapies on the future landscape of UTIs treatment, discussing possible advancements and challenges for further research. EXPERT OPINION: Comprehensions into the pathogenesis of UTIs have been gleaned from foundational basic science studies, laying the groundwork for the exploration of novel therapeutic interventions. The primary source of evidence originates predominantly from animal studies conducted on murine models. Nevertheless, the lack of clinical trials interferes the acquisition of robust evidence in humans. The challenges presented by the heterogeneity and virulence of uropathogens add an additional layer of complexity, posing an obstacle that scientists and clinicians are actively grappling with in their pursuit of effective solutions.

PDDGCN: A Parasitic Disease-Drug Association Predictor Based on Multi-view Fusion Graph Convolutional Network.

The precise identification of associations between diseases and drugs is paramount for comprehending the etiology and mechanisms underlying parasitic diseases. Computational approaches are highly effective in discovering and predicting disease-drug associations. However, the majority of these approaches primarily rely on link-based methodologies within distinct biomedical bipartite networks. In this study, we reorganized a fundamental dataset of parasitic disease-drug associations using the latest databases, and proposed a prediction model called PDDGCN, based on a multi-view graph convolutional network. To begin with, we fused similarity networks with binary networks to establish multi-view heterogeneous networks. We utilized neighborhood information aggregation layers to refine node embeddings within each view of the multi-view heterogeneous networks, leveraging inter- and intra-domain message passing to aggregate information from neighboring nodes. Subsequently, we integrated multiple embeddings from each view and fed them into the ultimate discriminator. The experimental results demonstrate that PDDGCN outperforms five state-of-the-art methods and four compared machine learning algorithms. Additionally, case studies have substantiated the effectiveness of PDDGCN in identifying associations between parasitic diseases and drugs. In summary, the PDDGCN model has the potential to facilitate the discovery of potential treatments for parasitic diseases and advance our comprehension of the etiology in this field. The source code is available at https://github.com/AhauBioinformatics/PDDGCN .

Predict lncRNA-drug associations based on graph neural network.

LncRNAs are an essential type of non-coding RNAs, which have been reported to be involved in various human pathological conditions. Increasing evidence suggests that drugs can regulate lncRNAs expression, which makes it possible to develop lncRNAs as therapeutic targets. Thus, developing in-silico methods to predict lncRNA-drug associations (LDAs) is a critical step for developing lncRNA-based therapies. In this study, we predict LDAs by using graph convolutional networks (GCN) and graph attention networks (GAT) based on lncRNA and drug similarity networks. Results show that our proposed method achieves good performance (average AUCs > 0.92) on five datasets. In addition, case studies and KEGG functional enrichment analysis further prove that the model can effectively identify novel LDAs. On the whole, this study provides a deep learning-based framework for predicting novel LDAs, which will accelerate the lncRNA-targeted drug development process.

OGNNMDA: a computational model for microbe-drug association prediction based on ordered message-passing graph neural networks.

In recent years, many excellent computational models have emerged in microbe-drug association prediction, but their performance still has room for improvement. This paper proposed the OGNNMDA framework, which applied an ordered message-passing mechanism to distinguish the different neighbor information in each message propagation layer, and it achieved a better embedding ability through deeper network layers. Firstly, the method calculates four similarity matrices based on microbe functional similarity, drug chemical structure similarity, and their respective Gaussian interaction profile kernel similarity. After integrating these similarity matrices, it concatenates the integrated similarity matrix with the known association matrix to obtain the microbe-drug heterogeneous matrix. Secondly, it uses a multi-layer ordered message-passing graph neural network encoder to encode the heterogeneous network and the known association information adjacency matrix, thereby obtaining the final embedding features of the microbe-drugs. Finally, it inputs the embedding features into the bilinear decoder to get the final prediction results. The OGNNMDA method performed comparative experiments, ablation experiments, and case studies on the aBiofilm, MDAD and DrugVirus datasets using 5-fold cross-validation. The experimental results showed that OGNNMDA showed the strongest prediction performance on aBiofilm and MDAD and obtained sub-optimal results on DrugVirus. In addition, the case studies on well-known drugs and microbes also support the effectiveness of the OGNNMDA method. Source codes and data are available at: https://github.com/yyzg/OGNNMDA.

Prediction of microbe-drug associations based on a modified graph attention variational autoencoder and random forest.

Introduction: The identification of microbe-drug associations can greatly facilitate drug research and development. Traditional methods for screening microbe-drug associations are time-consuming, manpower-intensive, and costly to conduct, so computational methods are a good alternative. However, most of them ignore the combination of abundant sequence, structural information, and microbe-drug network topology. Methods: In this study, we developed a computational framework based on a modified graph attention variational autoencoder (MGAVAEMDA) to infer potential microbedrug associations by combining biological information with the variational autoencoder. In MGAVAEMDA, we first used multiple databases, which include microbial sequences, drug structures, and microbe-drug association databases, to establish two comprehensive feature matrices of microbes and drugs after multiple similarity computations, fusion, smoothing, and thresholding. Then, we employed a combination of variational autoencoder and graph attention to extract low-dimensional feature representations of microbes and drugs. Finally, the lowdimensional feature representation and graphical adjacency matrix were input into the random forest classifier to obtain the microbe-drug association score to identify the potential microbe-drug association. Moreover, in order to correct the model complexity and redundant calculation to improve efficiency, we introduced a modified graph convolutional neural network embedded into the variational autoencoder for computing low dimensional features. Results: The experiment results demonstrate that the prediction performance of MGAVAEMDA is better than the five state-of-the-art methods. For the major measurements (AUC =0.9357, AUPR =0.9378), the relative improvements of MGAVAEMDA compared to the suboptimal methods are 1.76 and 1.47%, respectively. Discussion: We conducted case studies on two drugs and found that more than 85% of the predicted associations have been reported in PubMed. The comprehensive experimental results validated the reliability of our models in accurately inferring potential microbe-drug associations.

A Rare Case of Hair Dye Induced Oral Lichenoid Reaction.

Oral lichenoid lesions or reactions (OLLs/OLRs), which are clinical and histological contemporaries of the traditional oral lichen planus (OLP), had already garnered a great deal of attention in the literature. In contrast to idiopathic OLP, OLLs frequently have a definite, recognizable initiating component. Although a cursory clinical and histological evaluation of lesions frequently demonstrates numerous similarities with OLP, relatively new data has demonstrated that distinct features exist and serve as the foundation for the majority of categories. Although many systemic pharmaceuticals can lead to end oral lichenoid reactions, medications for diabetes, hypertension, nonsteroidal anti-inflammatory, antimalarial, and antifungal treatments are frequently blamed. Oral drugs, metallic dental restorations, acrylates, composite resins, glass ionomer cement, cinnamates, flavorings, and other chemical substances have all been associated when in direct contact. The objective of the case report is to elaborate the correlation between the oral lichenoid reaction and the use of hair dye. The incident under consideration is significant because the majority of past reports of allergic reactions to hair dye involved the face and scalp rather than the oral cavity. This report recommends that oral physicians inquire about the patient's use of cosmetics during history-taking whenever dealing with abrupt inflammatory responses in the orofacial area in order to diagnose and treat lesions more efficiently.

Schistosomicidal, hepatoprotective and antioxidant activities of the N-acetyl-L-cysteine and/or praziquantel in experimental acute mansonic schistosomiasis.

Schistosomiasis is a tropical parasitic disease, in which the major clinical manifestation includes hepatosplenomegaly, portal hypertension, and organs fibrosis. Clinically, treatment of schistosomiasis involves the use of praziquantel (PZQ) and supportive care, which does not improve the patient's outcome as liver injuries persist. Here, we report for the first time the effect of N-acetyl-L-cysteine (NAC) and/or praziquantel (PQZ) on S. mansoni, hepatic granuloma, serum markers for liver function and oxidative damage in acute schistosomiasis. Infected mice were divided into control, NAC, PZQ and NAC+PZQ groups and uninfected into control and NAC groups. After infection, NAC (200 mg/kg/day) was administrated until the 60th day and PZQ (100 mg/kg/day) from the 45th to the 49th day, both orally. On day 61, the mice were euthanized for serum markers for liver function. Worms were recovered, fragments of intestine employed to ascertain the oviposition pattern, and the liver was used for histopathological analysis, histomorphometry, egg and granuloma counting and oxidative stress marker assays. NAC reduced the burden of worms and eggs and increased the dead eggs in intestinal tissue. NAC+PZQ brought about reduction in granulomatous infiltration and NAC and/or PZQ reduced levels of ALT, AST, and alkaline phosphatase and increased albumin. NAC, PZQ or NAC+PZQ reduced levels of the superoxide anion, lipid peroxidation and protein carbonyl and increased sulfhydryl groups. The reduction in parasitological parameters, granulomatous inflammation and oxy-redox imbalance suggests NAC acts as a adjuvant in treatment of acute experimental schistosomiasis.

MDSVDNV: predicting microbe-drug associations by singular value decomposition and Node2vec.

Introduction: Recent researches have demonstrated that microbes are crucial for the growth and development of the human body, the movement of nutrients, and human health. Diseases may arise as a result of disruptions and imbalances in the microbiome. The pathological investigation of associated diseases and the advancement of clinical medicine can both benefit from the identification of drug-associated microbes. Methods: In this article, we proposed a new prediction model called MDSVDNV to infer potential microbe-drug associations, in which the Node2vec network embedding approach and the singular value decomposition (SVD) matrix decomposition method were first adopted to produce linear and non-linear representations of microbe interactions. Results and discussion: Compared with state-of-the-art competitive methods, intensive experimental results demonstrated that MDSVDNV could achieve the best AUC value of 98.51% under a 5-fold CV, which indicated that MDSVDNV outperformed existing competing models and may be an effective method for discovering latent microbe-drug associations in the future.

Machine learning in the development of targeting microRNAs in human disease.

A microRNA is a small, single-stranded, non-coding ribonucleic acid that plays a crucial role in RNA silencing and can regulate gene expression. With the in-depth study of miRNA in development and disease, miRNA has become an attractive target for novel therapeutic strategies. Exploring miRNA targeting therapy only through experiments is expensive and laborious, so it is essential to develop novel and efficient computational methods to narrow down the search. Recent advances in machine learning applied in biomedical informatics provide opportunities to explore miRNA-targeting drugs, thus promoting miRNA therapeutics. This review provides an overview of recent advancements in miRNA targeting therapeutic using machine learning. First, we mainly describe the basics of predicting miRNA targeting drugs, including pharmacogenomic data resources and data preprocessing. Then we present primary machine learning algorithms and elaborate their application in discovering relationships among miRNAs, drugs, and diseases. Along with the progress of miRNA targeting therapeutics, we finally analyze and discuss the current challenges and opportunities that machine learning confronts.

Discovery of Potential Therapeutic Drugs for COVID-19 Through Logistic Matrix Factorization With Kernel Diffusion.

Coronavirus disease 2019 (COVID-19) is rapidly spreading. Researchers around the world are dedicated to finding the treatment clues for COVID-19. Drug repositioning, as a rapid and cost-effective way for finding therapeutic options from available FDA-approved drugs, has been applied to drug discovery for COVID-19. In this study, we develop a novel drug repositioning method (VDA-KLMF) to prioritize possible anti-SARS-CoV-2 drugs integrating virus sequences, drug chemical structures, known Virus-Drug Associations, and Logistic Matrix Factorization with Kernel diffusion. First, Gaussian kernels of viruses and drugs are built based on known VDAs and nearest neighbors. Second, sequence similarity kernel of viruses and chemical structure similarity kernel of drugs are constructed based on biological features and an identity matrix. Third, Gaussian kernel and similarity kernel are diffused. Forth, a logistic matrix factorization model with kernel diffusion is proposed to identify potential anti-SARS-CoV-2 drugs. Finally, molecular dockings between the inferred antiviral drugs and the junction of SARS-CoV-2 spike protein-ACE2 interface are implemented to investigate the binding abilities between them. VDA-KLMF is compared with two state-of-the-art VDA prediction models (VDA-KATZ and VDA-RWR) and three classical association prediction methods (NGRHMDA, LRLSHMDA, and NRLMF) based on 5-fold cross validations on viruses, drugs, and VDAs on three datasets. It obtains the best recalls, AUCs, and AUPRs, significantly outperforming other five methods under the three different cross validations. We observe that four chemical agents coming together on any two datasets, that is, remdesivir, ribavirin, nitazoxanide, and emetine, may be the clues of treatment for COVID-19. The docking results suggest that the key residues K353 and G496 may affect the binding energies and dynamics between the inferred anti-SARS-CoV-2 chemical agents and the junction of the spike protein-ACE2 interface. Integrating various biological data, Gaussian kernel, similarity kernel, and logistic matrix factorization with kernel diffusion, this work demonstrates that a few chemical agents may assist in drug discovery for COVID-19.

Generalized matrix factorization based on weighted hypergraph learning for microbe-drug association prediction.

The complex and diverse microbial communities are closely related to human health, and the research of microbial communities plays an increasingly critical role in drug development and precision medicine. Identifying potential microbe-drug associations not only benefits drug discovery and clinical therapy, but also contributes to a better understanding of the mechanisms of action of microbes. Compared with the complexity and high cost of biological experiments, computational methods can quickly and efficiently predict potential microbe-drug associations, which could be a useful complement to experimental methods. In this study, we propose a generalized matrix factorization based on weighted hypergraph learning, WHGMF, to predict potential microbial-drug associations. First, we integrate multi-omics data to compute multiple features of microbes and drugs, including functional and semantic similarity of microbes, structural similarity of drugs, and microbe-drug association information. Second, the hypergraph is constructed by using strong neighborhood information, and to improve the performance of the hypergraph, the simple volume is adopted to calculate the hyperedge weight. Finally, hypergraph regularization is introduced for the generalized matrix factorization model, and high-order structural information is used to improve the representation ability of low-dimensional features. Results from multiple experiments demonstrate that WHGMF not only accurately predicts potential microbe-drug associations, but also has considerable adaptability to class-imbalanced datasets. In addition, WHGMF is also suitable for the prediction of new drugs and new microbes. A case study further demonstrates the effectiveness of our method. The code and data in this study are freely available at https://github.com/Mayingjun20179/WHGMF.

COVID-19 Drug Repurposing: A Network-Based Framework for Exploring Biomedical Literature and Clinical Trials for Possible Treatments.

BACKGROUND: With the Coronavirus becoming a new reality of our world, global efforts continue to seek answers to many questions regarding the spread, variants, vaccinations, and medications. Particularly, with the emergence of several strains (e.g., Delta, Omicron), vaccines will need further development to offer complete protection against the new variants. It is critical to identify antiviral treatments while the development of vaccines continues. In this regard, the repurposing of already FDA-approved drugs remains a major effort. In this paper, we investigate the hypothesis that a combination of FDA-approved drugs may be considered as a candidate for COVID-19 treatment if (1) there exists an evidence in the COVID-19 biomedical literature that suggests such a combination, and (2) there is match in the clinical trials space that validates this drug combination. METHODS: We present a computational framework that is designed for detecting drug combinations, using the following components (a) a Text-mining module: to extract drug names from the abstract section of the biomedical publications and the intervention/treatment sections of clinical trial records. (b) a network model constructed from the drug names and their associations, (c) a clique similarity algorithm to identify candidate drug treatments. RESULT AND CONCLUSIONS: Our framework has identified treatments in the form of two, three, or four drug combinations (e.g., hydroxychloroquine, doxycycline, and azithromycin). The identifications of the various treatment candidates provided sufficient evidence that supports the trustworthiness of our hypothesis.