ACDMBI: A deep learning model based on community division and multi-source biological information fusion predicts essential proteins.

Accurately identifying essential proteins is vital for drug research and disease diagnosis. Traditional centrality methods and machine learning approaches often face challenges in accurately discerning essential proteins, primarily relying on information derived from protein-protein interaction (PPI) networks. Despite attempts by some researchers to integrate biological data and PPI networks for predicting essential proteins, designing effective integration methods remains a challenge. In response to these challenges, this paper presents the ACDMBI model, specifically designed to overcome the aforementioned issues. ACDMBI is comprised of two key modules: feature extraction and classification. In terms of capturing relevant information, we draw insights from three distinct data sources. Initially, structural features of proteins are extracted from the PPI network through community division. Subsequently, these features are further optimized using Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT). Moving forward, protein features are extracted from gene expression data utilizing Bidirectional Long Short-Term Memory networks (BiLSTM) and a multi-head self-attention mechanism. Finally, protein features are derived by mapping subcellular localization data to a one-dimensional vector and processing it through fully connected layers. In the classification phase, we integrate features extracted from three different data sources, crafting a multi-layer deep neural network (DNN) for protein classification prediction. Experimental results on brewing yeast data showcase the ACDMBI model's superior performance, with AUC reaching 0.9533 and AUPR reaching 0.9153. Ablation experiments further reveal that the effective integration of features from diverse biological information significantly boosts the model's performance.

AE-RW: Predicting miRNA-disease associations by using autoencoder and random walk on miRNA-gene-disease heterogeneous network.

Since scientific investigations have demonstrated that aberrant expression of miRNAs brings about the incidence of numerous intricate diseases, precise determination of miRNA-disease relationships greatly contributes to the advancement of human medical progress. To tackle the issue of inefficient conventional experimental approaches, numerous computational methods have been proposed to predict miRNA-disease association with enhanced accuracy. However, constructing miRNA-gene-disease heterogeneous network by incorporating gene information has been relatively under-explored in existing computational techniques. Accordingly, this paper puts forward a technique to predict miRNA-disease association by applying autoencoder and implementing random walk on miRNA-gene-disease heterogeneous network(AE-RW). Firstly, we integrate association information and similarities between miRNAs, genes, and diseases to construct a miRNA-gene-disease heterogeneous network. Subsequently, we consolidate two network feature representations extracted independently via an autoencoder and a random walk procedure. Finally, deep neural network(DNN) are utilized to conduct association prediction. The experimental results demonstrate that the AE-RW model achieved an AUC of 0.9478 through 5-fold CV on the HMDD v3.2 dataset, outperforming the five most advanced existing models. Additionally, case studies were implemented for breast and lung cancer, further validated the superior predictive capabilities of our model.

RDGAN: Prediction of circRNA-Disease Associations Via Resistance Distance and Graph Attention Network.

As a series of single-stranded RNAs, circRNAs have been implicated in numerous diseases and can serve as valuable biomarkers for disease therapy and prevention. However, traditional biological experiments demand significant time and effort. Therefore, various computational methods have been proposed to address this limitation, but how to extract features more comprehensively remains a challenge that needs further attention in the future. In this study, we propose a unique approach to predict circRNA-disease associations based on resistance distance and graph attention network (RDGAN). Firstly, the associations of circRNA and disease are obtained by fusing multiple databases, and resistance distance as a similarity matrix is used to further deal with the sparse of the similarity matrices. Then the circRNA-disease heterogeneous network is constructed based on the similiarity of circRNA-circRNA, disease-disease and the known circRNA-disease adjacency matric. Secondly, leveraging the three neural network modules-ResGatedGraphConv, GAT and MFConv-we gather node feature embeddings collected from the heterogeneous network. Subsequently, all the characteristics are supplied to the self-attention mechanism to predict new potential connections. Finally, our model obtains a remarkable AUC value of 0.9630 through five- fold cross-validation, surpassing the predictive performance of the other eight state-of-the-art models.

GEHGAN: CircRNA-disease association prediction via graph embedding and heterogeneous graph attention network.

There is growing proof suggested that circRNAs play a crucial function in diverse important biological reactions related to human diseases. Within the area of biochemistry, a massive range of wet experiments have been carried out to find out the connections of circRNA-disease in recent years. Since wet experiments are expensive and laborious, nowadays, calculation-based solutions have increasingly attracted the attention of researchers. However, the performance of these methods is restricted due to the inability to balance the distribution among various types of nodes. To remedy the problem, we present a novel computational method called GEHGAN to forecast the new relationships in this research, leveraging graph embedding and heterogeneous graph attention networks. Firstly, we calculate circRNA sequences similarity, circRNA RBP similarity, disease semantic similarity and corresponding GIP kernel similarity to construct heterogeneous graph. Secondly, a graph embedding method using random walks with jump and stay strategies is applied to obtain the preliminary embeddings of circRNAs and diseases, greatly improving the performance of the model. Thirdly, a multi-head graph attention network is employed to further update the embeddings, followed by the employment of the MLP as a predictor. As a result, the five-fold cross-validation indicates that GEHGAN achieves an outstanding AUC score of 0.9829 and an AUPR value of 0.9815 on the CircR2Diseasev2.0 database, and case studies on osteosarcoma, gastric and colorectal neoplasms further confirm the model's efficacy at identifying circRNA-disease correlations.

Predicting associations between CircRNA and diseases through structure-aware graph transformer and path-integral convolution.

A series of biological experiments has demonstrated that circular RNAs play a crucial regulatory role in cellular processes and may be potentially associated with diseases. Uncovering these connections helps in understanding potential disease mechanisms and advancing the development of treatment strategies. However, in biology, traditional experiments face limitations in terms of efficiency and cost, especially when enumerating possible associations. To address these limitations, several computational methods have been proposed, but existing methods only measure from a nodal perspective and cannot capture structural similarities between edges. In this study, we introduce an advanced computational method called SATPIC2CD for analyzing potential associations between circular RNAs and diseases. Specifically, we first employ an Structure-Aware Graph Transformer (SAT), which extracts five predefined metapath representations before calculating attention. This adaptive network integrates structural information into the original self-attention by aggregating information within and between paths. Subsequently, we use Path Integral Convolutional Networks (PACN) to integrate feature information for all path weights between two nodes. Afterward, we complement the network node features with feature loss and feature smoothing using Gated Recurrent Units (GRU) and node centrality. Finally, a Multi-Layer Perceptron (MLP) is employed to obtain the ultimate prediction scores for each circular RNA-disease pair. SATPIC2CD performs remarkably well, with an accuracy of up to 0.9715 measured by the Area Under the Curve (AUC) in a 5-fold cross-validation, surpassing other comparative models. Case studies further emphasize the high precision of our method in identifying circular RNA-disease associations, laying a solid foundation for guiding future biological research efforts.

MGDHGS: Gene-bridged metabolite-disease relationships prediction via GraphSAGE and self-attention mechanism.

Metabolites represent the underlying information of biological systems. Revealing the links between metabolites and diseases can facilitate the development of targeted drugs. Traditional biological experiments can be used to validate the relationships of metabolite-disease, but these methods are time-consuming and labor-intensive. In contrast, the prevailing computational methods have improved efficiency but primarily rely on the metabolite-disease interactions, overlooking the impact of other biological components. To remedy the problem, we present a novel computational framework (MGDHGS) based on metabolite-gene-disease heterogeneous network to forecast potential associations. Specifically, we initially integrate data from multiple sources to construct metabolite-gene-disease heterogeneous network that includes known associations and computationally-derived similarities. Then, the GraphSAGE is harnessed to learn the low dimensional neighborhood representation in the heterogeneous network and self-attention mechanism is applied to effectively capture the connectivity patterns, which contributions to combine with nodes intrinsic and extrinsic features. Finally, the ultimate relationships probability scores are predicted by linear regression based on the these characteristics. The five-fold cross-validation showcases impressive AUC (0.9734) and PR (0.9718) for MGDHGS compared with five state-of-the-art methods, and the case studies validate that the metabolite-disease associations predicted by MGDHGS can be substantiated through pertinent biological experiments. The findings of this study show great potential contribution in the development of targeted drugs as well as offering solid support for our understanding of the complex interactions between metabolites, genes and diseases.

AMPCDA: Prediction of circRNA-disease associations by utilizing attention mechanisms on metapaths.

Researchers have been creating an expanding corpus of experimental evidences in biomedical field which has revealed prevalent associations between circRNAs and human diseases. Such linkages unveiled afforded a new perspective for elucidating etiology and devise innovative therapeutic strategies. In recent years, many computational methods were introduced to remedy the limitations of inefficiency and exorbitant budgets brought by conventional lab-experimental approaches to enumerate possible circRNA-disease associations, but the majority of existing methods still face challenges in effectively integrating node embeddings with higher-order neighborhood representations, which might hinder the final predictive accuracy from attaining optimal measures. To overcome such constraints, we proposed AMPCDA, a computational technique harnessing predefined metapaths to predict circRNA-disease associations. Specifically, an association graph is initially built upon three source databases and two similarity derivation procedures, and DeepWalk is subsequently imposed on the graph to procure initial feature representations. Vectorial embeddings of metapath instances, concatenated by initial node features, are then fed through a customed encoder. By employing self-attention section, metapath-specific contributions to each node are accumulated before combining with node's intrinsic features and channeling into a graph attention module, which furnished the input representations for the multilayer perceptron to predict the ultimate association probability scores. By integrating graph topology features and node embedding themselves, AMPCDA managed to effectively leverage information carried by multiple nodes along paths and exhibited an exceptional predictive performance, achieving AUC values of 0.9623, 0.9675, and 0.9711 under 5-fold cross validation, 10-fold cross validation, and leave-one-out cross validation, respectively. These results signify substantial accuracy improvements compared to other prediction models. Case study assessments confirm the high predictive accuracy of our proposed technique in identifying circRNA-disease connections, highlighting its value in guiding future biological research to reveal new disease mechanisms.

Effects of T-2 toxin on the muscle proteins of shrimp (Litopenaeus vannamei) - a proteomics study.

BACKGROUND: T-2 toxin (T-2) is a potent mycotoxin and a common contaminant of aquatic animal feed, posing a serious risk to health and aquatic animals. We investigated the effect of T-2 on shrimp muscle proteins using proteomics and conventional biochemical methods. Shrimp were fed a diet containing T-2 at 0-12.2 mg kg-1 for 20 days, and changes to the muscle protein composition, ATPase activities, and the sulfhydryl (SH) content and hydrophobicity of actomyosin (AM) were determined. A proteomics study of the proteins was conducted with sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE), two-dimensional (2D) electrophoresis, and matrix-assisted laser desorption/ionization - time of flight mass spectrometry (MALDI-TOF/TOF MS). RESULTS: Exposure to T-2 markedly affected the muscle protein composition of shrimp in a concentration-responsive manner that displayed a diphasic effect. At a low T-2 concentration (1.2 mg kg-1 ), the levels of three major muscle proteins (myofibrillar, sarcoplasmic, and stroma) increased but at higher concentrations they declined progressively. T-2 exposure also led to a breakdown of muscle proteins as evidenced by increases in alkali-soluble protein and the surface hydrophobicity (SoANS) of AM. Thirty differentially expressed proteins were detected, 12 of which showed a concentration-response relationship with T-2 exposure. Among them, 11 homologous proteins were identified by mass spectrometry (MS), with several being key enzymes in energy metabolism. CONCLUSION: This study demonstrated that T-2 exposure at medium to high concentrations could significantly affect the protein composition and quality of shrimp muscle, and potentially some of its key metabolisms. One of the arginine kinases (spot 27) was particularly responsive to T-2 and could potentially be used as a biomarker protein for T-2 intoxication by shrimp. © 2019 Society of Chemical Industry.

Biotransformation enzyme activities and phase I metabolites analysis in Litopenaeus vannamei following intramuscular administration of T-2 toxin.

T-2 toxin (T-2) is a type-A trichothecene produced by Fusarium that causes toxicity to animals. T-2 contamination of grain-based aquatic feed is a concern for the industries related to edible aquatic crustacean species such as the shrimp industry because it can lead to serious food safety issues. T-2, its metabolites, and selected phase I (EROD, CarE) and phase II (GST, UGT, SULT) detoxification enzymes in hemolymph and tissues were monitored at 0, 5, 10 15, 30, 45, and 60 min following T-2 intramuscular administration (3 mg/kg bw) in shrimp (Litopenaeus vannamei). Marked increases of EROD activity in hepatopancreas and CarE activity in hemolymph, gill, hepatopancreas and intestine were observed followed by increases in phase II enzymes (GST, UGT, SULT) in hepatopancreas, hemolymph, intestine and gill, which remained elevated for an extended period. Time-dependent decrease in shrimp tissue T-2 concentration was observed. HT-2 increased up to 15 min. Most other T-2 metabolites were detected but not T-2 tetraol. Enzyme responses on exposure to T-2 were tissue-specific and time-dependent. Detection results indicated that HT-2 may not be the only important metabolite in aquatic crustacean species. Further investigation into T-2 metabolite toxicity is needed to fully understand the food safety issues related to T-2.

Selection and Evaluation of Indexes Commonly Used to Determine Contamination with T-2 Toxin in Pacific White Shrimp Litopenaeus vannamei by the Grey Relational Method.

The objectives of the present study were to evaluate the effects of different concentrations of the mycotoxin T-2 toxin in feed on muscle performance in the Pacific white shrimp Litopenaeus vannamei, evaluate indexes of physiological variables that indicate T-2 toxin contamination in the shrimp using the grey relational method, and determine the dose-response relationships between T-2 toxin and the indexes. Of the 6 physical, 7 biochemical, and 17 nutritional indexes examined, the values of the grey relational coefficients were highest for the hepatopancreas: body weight ratio (HBR), alanine aminotransferase (ALT) activity, and serine (SER) content (0.83, 0.68, and 0.82, respectively). Therefore, the HBR, ALT activity, and SER content were selected as appropriate indexes for contamination of Pacific white shrimp muscle with T-2 toxin. Based on their dose-response relationship curves, mean effective doses of 1.45, 1.69, and 1.33 mg of T-2 toxin/kg of feed were obtained for the HBR, ALT activity, and SER content, respectively. These results offer technical reference points for the evaluation and control of T-2 toxin in shrimp feed. Received April 28, 2016; accepted April 9, 2017.