Likelihood-based feature representation learning combined with neighborhood information for predicting circRNA-miRNA associations.

Connections between circular RNAs (circRNAs) and microRNAs (miRNAs) assume a pivotal position in the onset, evolution, diagnosis and treatment of diseases and tumors. Selecting the most potential circRNA-related miRNAs and taking advantage of them as the biological markers or drug targets could be conducive to dealing with complex human diseases through preventive strategies, diagnostic procedures and therapeutic approaches. Compared to traditional biological experiments, leveraging computational models to integrate diverse biological data in order to infer potential associations proves to be a more efficient and cost-effective approach. This paper developed a model of Convolutional Autoencoder for CircRNA-MiRNA Associations (CA-CMA) prediction. Initially, this model merged the natural language characteristics of the circRNA and miRNA sequence with the features of circRNA-miRNA interactions. Subsequently, it utilized all circRNA-miRNA pairs to construct a molecular association network, which was then fine-tuned by labeled samples to optimize the network parameters. Finally, the prediction outcome is obtained by utilizing the deep neural networks classifier. This model innovatively combines the likelihood objective that preserves the neighborhood through optimization, to learn the continuous feature representation of words and preserve the spatial information of two-dimensional signals. During the process of 5-fold cross-validation, CA-CMA exhibited exceptional performance compared to numerous prior computational approaches, as evidenced by its mean area under the receiver operating characteristic curve of 0.9138 and a minimal SD of 0.0024. Furthermore, recent literature has confirmed the accuracy of 25 out of the top 30 circRNA-miRNA pairs identified with the highest CA-CMA scores during case studies. The results of these experiments highlight the robustness and versatility of our model.

Discovering Consensus Regions for Interpretable Identification of RNA N6-Methyladenosine Modification Sites via Graph Contrastive Clustering.

As a pivotal post-transcriptional modification of RNA, N6-methyladenosine (m6A) has a substantial influence on gene expression modulation and cellular fate determination. Although a variety of computational models have been developed to accurately identify potential m6A modification sites, few of them are capable of interpreting the identification process with insights gained from consensus knowledge. To overcome this problem, we propose a deep learning model, namely M6A-DCR, by discovering consensus regions for interpretable identification of m6A modification sites. In particular, M6A-DCR first constructs an instance graph for each RNA sequence by integrating specific positions and types of nucleotides. The discovery of consensus regions is then formulated as a graph clustering problem in light of aggregating all instance graphs. After that, M6A-DCR adopts a motif-aware graph reconstruction optimization process to learn high-quality embeddings of input RNA sequences, thus achieving the identification of m6A modification sites in an end-to-end manner. Experimental results demonstrate the superior performance of M6A-DCR by comparing it with several state-of-the-art identification models. The consideration of consensus regions empowers our model to make interpretable predictions at the motif level. The analysis of cross validation through different species and tissues further verifies the consistency between the identification results of M6A-DCR and the evolutionary relationships among species.

Exploring the potential of a novel alternating current stimulated iron­carbon anammox process: A new horizon for nitrogen removal.

This study explored a novel alternating current (AC) stimulation approach to enhance the nitrogen removal efficiency of an iron­carbon based anammox (FeC anammox) system. In the preliminary experiment, the TN removal efficiency of the AC stimulated system was 8.06 % higher than that of a DC simulated system in same current densities of 0.25 mA/cm2. Gene expression analysis revealed that the AC-stimulated system, where, compared with the anammox system alone, the expression of HZS, HDH, NarG, NirS, NorB and NosZ increased by 1.81, 2.50, 1.64, 0.23, 1.15 and 1.27 times, respectively. In the continuous experiment, the TN removal rate increased from 60.13 % to 84.34 % after AC stimulation, and the working time of the FeC materials increased to 20 days. An analysis of the mechanism revealed that the parallel connection between the capacitive reactance and filler resistance in AC might reduce the internal resistance of the system, thereby improving the actual current density received by local microorganisms, and achieving a better strengthening effect.

Incorporating higher order network structures to improve miRNA-disease association prediction based on functional modularity.

As microRNAs (miRNAs) are involved in many essential biological processes, their abnormal expressions can serve as biomarkers and prognostic indicators to prevent the development of complex diseases, thus providing accurate early detection and prognostic evaluation. Although a number of computational methods have been proposed to predict miRNA-disease associations (MDAs) for further experimental verification, their performance is limited primarily by the inadequacy of exploiting lower order patterns characterizing known MDAs to identify missing ones from MDA networks. Hence, in this work, we present a novel prediction model, namely HiSCMDA, by incorporating higher order network structures for improved performance of MDA prediction. To this end, HiSCMDA first integrates miRNA similarity network, disease similarity network and MDA network to preserve the advantages of all these networks. After that, it identifies overlapping functional modules from the integrated network by predefining several higher order connectivity patterns of interest. Last, a path-based scoring function is designed to infer potential MDAs based on network paths across related functional modules. HiSCMDA yields the best performance across all datasets and evaluation metrics in the cross-validation and independent validation experiments. Furthermore, in the case studies, 49 and 50 out of the top 50 miRNAs, respectively, predicted for colon neoplasms and lung neoplasms have been validated by well-established databases. Experimental results show that rich higher order organizational structures exposed in the MDA network gain new insight into the MDA prediction based on higher order connectivity patterns.

Enhancement of iron-based nitrogen removal with an electric-magnetic field in an upflow microaerobic sludge reactor (UMSR).

Traditional denitrification often produces high operating costs and excessive sludge disposal expenses due to conventional carbon sources. A novel electric-magnetic field (MF) 48 mT with Fe0 and C-Fe0 powder in an upflow microaerobic sludge reactor (UMSR) improved nitrogen removal from wastewater without organic carbon resources and gave richness to the heterotrophic bacterial community. In the current study, the reactor was operated for 78 ± 2 days, divided into five stages (without Fe0, with Fe0, coupling with MF, without coupling with MF, and coupling with MF again), at a hydraulic retention time (HRT) of 2.5 h, with an influent loading of ammonium (NH4+-N) 50 ± 2 mg/L, at 25-27 °C, and less than 1.0 mg/L dissolved oxygen (DO). The results demonstrated nitrogen removal efficiency enhanced after coupling with MF on the levels of NO3--N by 76% with an effluent concentration of 8.7 mg/L, NH4+-N by 72% with an effluent concentration of 13.6 mg/L, and total nitrogen removal (TN) by 76%, respectively. After coupling the MF with the reactor, the microbial community data analysis showed the dominant abundance of ammonia-oxidizing bacteria, heterotrophic nitrifying bacteria, and denitrifying bacteria on the level of Anaerolineaceae_uncultured 2%, which is capable of denitrification that uses Fe2+ as an electron source, Gemmatimonadaceae_uncultured 4%, Hydrogenophaga 4% which is capable of catalyzing hydrogenotrophic denitrification and correlating to nitrate removal, denitrification and desulfurization bacteria SBR1031_norank 18%, anammox-bacteria Saccharimonadales_norank 2%, and (AOM) Limnobacter 3% in the sludge.

RLFDDA: a meta-path based graph representation learning model for drug-disease association prediction.

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.

A Novel Method to Predict Drug-Target Interactions Based on Large-Scale Graph Representation Learning.

Identification of drug-target interactions (DTIs) is a significant step in the drug discovery or repositioning process. Compared with the time-consuming and labor-intensive in vivo experimental methods, the computational models can provide high-quality DTI candidates in an instant. In this study, we propose a novel method called LGDTI to predict DTIs based on large-scale graph representation learning. LGDTI can capture the local and global structural information of the graph. Specifically, the first-order neighbor information of nodes can be aggregated by the graph convolutional network (GCN); on the other hand, the high-order neighbor information of nodes can be learned by the graph embedding method called DeepWalk. Finally, the two kinds of feature are fed into the random forest classifier to train and predict potential DTIs. The results show that our method obtained area under the receiver operating characteristic curve (AUROC) of 0.9455 and area under the precision-recall curve (AUPR) of 0.9491 under 5-fold cross-validation. Moreover, we compare the presented method with some existing state-of-the-art methods. These results imply that LGDTI can efficiently and robustly capture undiscovered DTIs. Moreover, the proposed model is expected to bring new inspiration and provide novel perspectives to relevant researchers.