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Background Drug repositioning is a very important task that provides critical information for exploring the potential efficacy of drugs. Yet developing computational models that can effectively predict drug–disease associations (DDAs) is still a challenging task. Previous studies suggest that the accuracy of DDA prediction can be improved by integrating different types of biological features. But how to conduct an effective integration remains a challenging problem for accurately discovering new indications for approved drugs. Methods In this paper, we propose a novel meta-path based graph representation learning model, namely RLFDDA, to predict potential DDAs on heterogeneous biological networks. RLFDDA first calculates drug–drug similarities and disease–disease similarities as the intrinsic biological features of drugs and diseases. A heterogeneous network is then constructed by integrating DDAs, disease–protein associations and drug–protein associations. With such a network, RLFDDA adopts a meta-path random walk model to learn the latent representations of drugs and diseases, which are concatenated to construct joint representations of drug–disease associations. As the last step, we employ the random forest classifier to predict potential DDAs with their joint representations. Results To demonstrate the effectiveness of RLFDDA, we have conducted a series of experiments on two benchmark datasets by following a ten-fold cross-validation scheme. The results show that RLFDDA yields the best performance in terms of AUC and F1-score when compared with several state-of-the-art DDAs prediction models. We have also conducted a case study on two common diseases, i.e., paclitaxel and lung tumors, and found that 7 out of top-10 diseases and 8 out of top-10 drugs have already been validated for paclitaxel and lung tumors respectively with literature evidence. Hence, the promising performance of RLFDDA may provide a new perspective for novel DDAs discovery over heterogeneous networks.

Background Protein–protein interaction (PPI) plays an important role in regulating cells and signals. Despite the ongoing efforts of the bioassay group, continued incomplete data limits our ability to understand the molecular roots of human disease. Therefore, it is urgent to develop a computational method to predict PPIs from the perspective of molecular system. Methods In this paper, a highly efficient computational model, MTV-PPI, is proposed for PPI prediction based on a heterogeneous molecular network by learning inter-view protein sequences and intra-view interactions between molecules simultaneously. On the one hand, the inter-view feature is extracted from the protein sequence by k-mer method. On the other hand, we use a popular embedding method LINE to encode the heterogeneous molecular network to obtain the intra-view feature. Thus, the protein representation used in MTV-PPI is constructed by the aggregation of its inter-view feature and intra-view feature. Finally, random forest is integrated to predict potential PPIs. Results To prove the effectiveness of MTV-PPI, we conduct extensive experiments on a collected heterogeneous molecular network with the accuracy of 86.55%, sensitivity of 82.49%, precision of 89.79%, AUC of 0.9301 and AUPR of 0.9308. Further comparison experiments are performed with various protein representations and classifiers to indicate the effectiveness of MTV-PPI in predicting PPIs based on a complex network. Conclusion The achieved experimental results illustrate that MTV-PPI is a promising tool for PPI prediction, which may provide a new perspective for the future interactions prediction researches based on heterogeneous molecular network.

Background Drug repositioning, meanings finding new uses for existing drugs, which can accelerate the processing of new drugs research and development. Various computational methods have been presented to predict novel drug–disease associations for drug repositioning based on similarity measures among drugs and diseases. However, there are some known associations between drugs and diseases that previous studies not utilized. Methods In this work, we develop a deep gated recurrent units model to predict potential drug–disease interactions using comprehensive similarity measures and Gaussian interaction profile kernel. More specifically, the similarity measure is used to exploit discriminative feature for drugs based on their chemical fingerprints. Meanwhile, the Gaussian interactions profile kernel is employed to obtain efficient feature of diseases based on known disease-disease associations. Then, a deep gated recurrent units model is developed to predict potential drug–disease interactions. Results The performance of the proposed model is evaluated on two benchmark datasets under tenfold cross-validation. And to further verify the predictive ability, case studies for predicting new potential indications of drugs were carried out. Conclusion The experimental results proved the proposed model is a useful tool for predicting new indications for drugs or new treatments for diseases, and can accelerate drug repositioning and related drug research and discovery.

Background As an important task in bioinformatics, clustering analysis plays a critical role in understanding the functional mechanisms of many complex biological systems, which can be modeled as biological networks. The purpose of clustering analysis in biological networks is to identify functional modules of interest, but there is a lack of online clustering tools that visualize biological networks and provide in-depth biological analysis for discovered clusters. Results Here we present BioCAIV, a novel webserver dedicated to maximize its accessibility and applicability on the clustering analysis of biological networks. This, together with its user-friendly interface, assists biological researchers to perform an accurate clustering analysis for biological networks and identify functionally significant modules for further assessment. Conclusions BioCAIV is an efficient clustering analysis webserver designed for a variety of biological networks. BioCAIV is freely available without registration requirements at http://bioinformatics.tianshanzw.cn:8888/BioCAIV/ .

Abnormal shifts in global climate, leading to extreme weather, significantly threaten the safety of individuals involved in outdoor activities. Hypothermia-induced coma or death frequently occurs in clinical and forensic settings. Despite this, the precise mechanism of central nervous system injury due to hypothermia remains unclear, hindering the development of targeted clinical treatments and specific forensic diagnostic indicators. The GEO database was searched to identify datasets related to hypothermia. Post-bioinformatics analyses, DEGs, and ferroptosis-related DEGs (FerrDEGs) were intersected. GSEA was then conducted to elucidate the functions of the Ferr-related genes. Animal experiments conducted in this study demonstrated that hypothermia, compared to the control treatment, can induce significant alterations in iron death-related genes such as PPARG, SCD, ADIPOQ, SAT1, EGR1, and HMOX1 in cerebral cortex nerve cells. These changes lead to iron ion accumulation, lipid peroxidation, and marked expression of iron death-related proteins. The application of the iron death inhibitor Ferrostatin-1 (Fer-1) effectively modulates the expression of these genes, reduces lipid peroxidation, and improves the expression of iron death-related proteins. Severe hypothermia disrupts the metabolism of cerebral cortex nerve cells, causing significant alterations in ferroptosis-related genes. These genetic changes promote ferroptosis through multiple pathways.

This study differentiates myocardial infarction (MI) and strangulation death (STR) from the perspective of amino acid metabolism. In this study, MI mice model via subcutaneous injection of isoproterenol and STR mice model by neck strangulation were constructed, and were randomly divided into control (CON), STR, mild MI (MMI), and severe MI (SMI) groups. The metabolomics profiles were obtained by liquid chromatography-mass spectrometry (LC–MS)-based untargeted metabolomics. Principal component analysis, partial least squares-discriminant analysis, volcano plots, and heatmap were used for discrepancy metabolomics analysis. Pathway enrichment analysis was performed and the expression of proteins related to metabolomics was detected using immunohistochemical and western blot methods. Differential metabolites and metabolite pathways were screened. In addition, we found the expression of PPM1K was significantly reduced in the MI group, but the expression of p-mTOR and p-S6K1 were significantly increased (all P < 0.05), especially in the SMI group (P < 0.01). The expression of Cyt-C was significantly increased in each group compared with the CON group, especially in the STR group (all P < 0.01), and the expression of AMPKα1 was significantly increased in the STR group (all P < 0.01). Our study for the first time revealed significant differences in amino acid metabolism between STR and MI.

The genetic diversity and frequent recombination of coronavirus genomes render the variation of coronaviruses highly unpredictable. [...]exploring biomarkers of SARS‐CoV‐2 with a combination of integrated bioinformatics methods with expression profiling techniques is hopefully helpful for improving the diagnosis, treatment and prognosis of SARS‐CoV‐2 in the future. METHODS AND MATERIALS Data inclusion and DEG screening The gene expression profile of GSE147507 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE147507) in this study was obtained from National Center for Biotechnology Information Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo/), on the basis of GPL18573 platform of Illumina NextSeq 500 (Homo sapiens) and GPL28369 platform of Illumina NextSeq 500 (Mustela putorius furo). [...]we choose P < .05 as the standard to screen the hub genes. In the NHBE group, (P) Enriched functional BP of the target genes; (Q) Enriched CC of the target genes; (R) Enriched MF of the target genes; (S) Enriched KEGG pathways of the target genes GO function enrichment analysis of the DEGs Composed of the biological pathway (BP), the CC, and the MF, GO enrichment analysis for the DEGs in three groups of Calu‐3, A549 and NHBE were shown in Table S2 and Figure 1H‐J,L‐N,P‐R. KEGG pathway analysis KEGG enrichment analysis was conducted on all DEGs and corresponding P‐value and P‐adjust values of each pathway were obtained.

Abnormal climate change seriously endangers the safety of outdoor work and life, often causing hypothermia-induced coma or death. As the underlying mechanism has not been fully elucidated, a targeted treatment for hypothermia-triggered neuronal injury and forensic pathology indicators of fatal hypothermia are lacking. Herein, we aimed to explore hypothermia-induced changes in gene expression and metabolite profiles of cerebral cortical tissues to elucidate the mechanism of hypothermia-promoted necroptosis of cerebral cortical neurons. Flow cytometry and fluoro-jade C staining showed that low temperature caused necroptosis of cerebral cortical neurons. Transcriptomics identified 244 differential genes between hypothermia-exposed cortical tissue and control tissue. These genes were enriched in tumor necrosis factor (TNF)-α and nuclear factor (NF)-kappa B signaling pathways, as revealed by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Broadly targeted metabolomics identified 49 differential metabolites with significant differences. N -alpha-acetyl-L-arginine, argininosuccinic acid, glutaric acid, and other ornithine cycle-associated metabolites were significantly reduced in the hypothermia-exposed cortical tissue, driving fumaric acid reduction in the tricarboxylic acid (TCA) cycle. In addition, KEGG enrichment analysis showed significant changes in the TCA cycle pathway. A combined transcriptomic and metabolomic analysis uncovered that hypothermia induced oxidative stress through NF-κB activation, caused mitochondrial damage, impaired the ornithine cycle, and, ultimately, induced neuronal necroptosis. Pharmacological inhibition of NF-κB by the SC75741 inhibitor effectively ameliorated hypothermia-triggered necroptosis. In conclusion, our results suggest that the NF-κB transcription factor is a potential marker of hypothermia-induced neuronal necroptosis in the mouse cerebral cortex. In addition, our findings indicate the mechanism of necroptosis in cerebral cortical neurons caused by low temperature, which is beneficial for our understanding of hypothermia-induced coma and death. Highlights Combined transcriptomic and metabolomic analysis reveal key mechanisms by which hypothermia-induced neuronal necroptosis occurs in cerebral cortical tissue. Necroptosis is the main type of hypothermia-induced neuronal death in cerebral cortical tissue. Oxidative stress is the main mechanism by which hypothermia induces neuronal necroptosis in cerebral cortical tissue. NF-κB–inducible nitric oxide synthase (iNOS) signaling pathways are key targets of hypothermia-induced neuronal necroptosis in cerebral cortical tissue.

Drug repositioning is a very important task that provides critical information for exploring the potential efficacy of drugs. Yet developing computational models that can effectively predict drug-disease associations (DDAs) is still a challenging task. Previous studies suggest that the accuracy of DDA prediction can be improved by integrating different types of biological features. But how to conduct an effective integration remains a challenging problem for accurately discovering new indications for approved drugs. In this paper, we propose a novel meta-path based graph representation learning model, namely RLFDDA, to predict potential DDAs on heterogeneous biological networks. RLFDDA first calculates drug-drug similarities and disease-disease similarities as the intrinsic biological features of drugs and diseases. A heterogeneous network is then constructed by integrating DDAs, disease-protein associations and drug-protein associations. With such a network, RLFDDA adopts a meta-path random walk model to learn the latent representations of drugs and diseases, which are concatenated to construct joint representations of drug-disease associations. As the last step, we employ the random forest classifier to predict potential DDAs with their joint representations. To demonstrate the effectiveness of RLFDDA, we have conducted a series of experiments on two benchmark datasets by following a ten-fold cross-validation scheme. The results show that RLFDDA yields the best performance in terms of AUC and F1-score when compared with several state-of-the-art DDAs prediction models. We have also conducted a case study on two common diseases, i.e., paclitaxel and lung tumors, and found that 7 out of top-10 diseases and 8 out of top-10 drugs have already been validated for paclitaxel and lung tumors respectively with literature evidence. Hence, the promising performance of RLFDDA may provide a new perspective for novel DDAs discovery over heterogeneous networks.

Protein-protein interaction (PPI) plays an important role in regulating cells and signals. Despite the ongoing efforts of the bioassay group, continued incomplete data limits our ability to understand the molecular roots of human disease. Therefore, it is urgent to develop a computational method to predict PPIs from the perspective of molecular system. In this paper, a highly efficient computational model, MTV-PPI, is proposed for PPI prediction based on a heterogeneous molecular network by learning inter-view protein sequences and intra-view interactions between molecules simultaneously. On the one hand, the inter-view feature is extracted from the protein sequence by k-mer method. On the other hand, we use a popular embedding method LINE to encode the heterogeneous molecular network to obtain the intra-view feature. Thus, the protein representation used in MTV-PPI is constructed by the aggregation of its inter-view feature and intra-view feature. Finally, random forest is integrated to predict potential PPIs. To prove the effectiveness of MTV-PPI, we conduct extensive experiments on a collected heterogeneous molecular network with the accuracy of 86.55%, sensitivity of 82.49%, precision of 89.79%, AUC of 0.9301 and AUPR of 0.9308. Further comparison experiments are performed with various protein representations and classifiers to indicate the effectiveness of MTV-PPI in predicting PPIs based on a complex network. The achieved experimental results illustrate that MTV-PPI is a promising tool for PPI prediction, which may provide a new perspective for the future interactions prediction researches based on heterogeneous molecular network.