DeepFGRN: inference of gene regulatory network with regulation type based on directed graph embedding.

The inference of gene regulatory networks (GRNs) from gene expression profiles has been a key issue in systems biology, prompting many researchers to develop diverse computational methods. However, most of these methods do not reconstruct directed GRNs with regulatory types because of the lack of benchmark datasets or defects in the computational methods. Here, we collect benchmark datasets and propose a deep learning-based model, DeepFGRN, for reconstructing fine gene regulatory networks (FGRNs) with both regulation types and directions. In addition, the GRNs of real species are always large graphs with direction and high sparsity, which impede the advancement of GRN inference. Therefore, DeepFGRN builds a node bidirectional representation module to capture the directed graph embedding representation of the GRN. Specifically, the source and target generators are designed to learn the low-dimensional dense embedding of the source and target neighbors of a gene, respectively. An adversarial learning strategy is applied to iteratively learn the real neighbors of each gene. In addition, because the expression profiles of genes with regulatory associations are correlative, a correlation analysis module is designed. Specifically, this module not only fully extracts gene expression features, but also captures the correlation between regulators and target genes. Experimental results show that DeepFGRN has a competitive capability for both GRN and FGRN inference. Potential biomarkers and therapeutic drugs for breast cancer, liver cancer, lung cancer and coronavirus disease 2019 are identified based on the candidate FGRNs, providing a possible opportunity to advance our knowledge of disease treatments.

Inference of gene regulatory networks based on directed graph convolutional networks.

Inferring gene regulatory network (GRN) is one of the important challenges in systems biology, and many outstanding computational methods have been proposed; however there remains some challenges especially in real datasets. In this study, we propose Directed Graph Convolutional neural network-based method for GRN inference (DGCGRN). To better understand and process the directed graph structure data of GRN, a directed graph convolutional neural network is conducted which retains the structural information of the directed graph while also making full use of neighbor node features. The local augmentation strategy is adopted in graph neural network to solve the problem of poor prediction accuracy caused by a large number of low-degree nodes in GRN. In addition, for real data such as E.coli, sequence features are obtained by extracting hidden features using Bi-GRU and calculating the statistical physicochemical characteristics of gene sequence. At the training stage, a dynamic update strategy is used to convert the obtained edge prediction scores into edge weights to guide the subsequent training process of the model. The results on synthetic benchmark datasets and real datasets show that the prediction performance of DGCGRN is significantly better than existing models. Furthermore, the case studies on bladder uroepithelial carcinoma and lung cancer cells also illustrate the performance of the proposed model.

Double matrix completion for circRNA-disease association prediction.

BACKGROUND: Circular RNAs (circRNAs) are a class of single-stranded RNA molecules with a closed-loop structure. A growing body of research has shown that circRNAs are closely related to the development of diseases. Because biological experiments to verify circRNA-disease associations are time-consuming and wasteful of resources, it is necessary to propose a reliable computational method to predict the potential candidate circRNA-disease associations for biological experiments to make them more efficient. RESULTS: In this paper, we propose a double matrix completion method (DMCCDA) for predicting potential circRNA-disease associations. First, we constructed a similarity matrix of circRNA and disease according to circRNA sequence information and semantic disease information. We also built a Gauss interaction profile similarity matrix for circRNA and disease based on experimentally verified circRNA-disease associations. Then, the corresponding circRNA sequence similarity and semantic similarity of disease are used to update the association matrix from the perspective of circRNA and disease, respectively, by matrix multiplication. Finally, from the perspective of circRNA and disease, matrix completion is used to update the matrix block, which is formed by splicing the association matrix obtained in the previous step with the corresponding Gaussian similarity matrix. Compared with other approaches, the model of DMCCDA has a relatively good result in leave-one-out cross-validation and five-fold cross-validation. Additionally, the results of the case studies illustrate the effectiveness of the DMCCDA model. CONCLUSION: The results show that our method works well for recommending the potential circRNAs for a disease for biological experiments.

Identification of driver genes based on gene mutational effects and network centrality.

BACKGROUND: As one of the deadliest diseases in the world, cancer is driven by a few somatic mutations that disrupt the normal growth of cells, and leads to abnormal proliferation and tumor development. The vast majority of somatic mutations did not affect the occurrence and development of cancer; thus, identifying the mutations responsible for tumor occurrence and development is one of the main targets of current cancer treatments. RESULTS: To effectively identify driver genes, we adopted a semi-local centrality measure and gene mutation effect function to assess the effect of gene mutations on changes in gene expression patterns. Firstly, we calculated the mutation score for each gene. Secondly, we identified differentially expressed genes (DEGs) in the cohort by comparing the expression profiles of tumor samples and normal samples, and then constructed a local network for each mutation gene using DEGs and mutant genes according to the protein-protein interaction network. Finally, we calculated the score of each mutant gene according to the objective function. The top-ranking mutant genes were selected as driver genes. We name the proposed method as mutations effect and network centrality. CONCLUSIONS: Four types of cancer data in The Cancer Genome Atlas were tested. The experimental data proved that our method was superior to the existing network-centric method, as it was able to quickly and easily identify driver genes and rare driver factors.

MEFFGRN: Matrix enhancement and feature fusion-based method for reconstructing the gene regulatory network of epithelioma papulosum cyprini cells by spring viremia of carp virus infection.

Gene regulatory networks (GRNs) are crucial for understanding organismal molecular mechanisms and processes. Construction of GRN in the epithelioma papulosum cyprini (EPC) cells of cyprinid fish by spring viremia of carp virus (SVCV) infection helps understand the immune regulatory mechanisms that enhance the survival capabilities of cyprinid fish. Although many computational methods have been used to infer GRNs, specialized approaches for predicting the GRN of EPC cells following SVCV infection are lacking. In addition, most existing methods focus primarily on gene expression features, neglecting the valuable network structural information in known GRNs. In this study, we propose a novel supervised deep neural network, named MEFFGRN (Matrix Enhancement- and Feature Fusion-based method for Gene Regulatory Network inference), to accurately predict the GRN of EPC cells following SVCV infection. MEFFGRN considers both gene expression data and network structure information of known GRN and introduces a matrix enhancement method to address the sparsity issue of known GRN, extracting richer network structure information. To optimize the benefits of CNN (Convolutional Neural Network) in image processing, gene expression and enhanced GRN data were transformed into histogram images for each gene pair respectively. Subsequently, these histograms were separately fed into CNNs for training to obtain the corresponding gene expression and network structural features. Furthermore, a feature fusion mechanism was introduced to comprehensively integrate the gene expression and network structural features. This integration considers the specificity of each feature and their interactive information, resulting in a more comprehensive and precise feature representation during the fusion process. Experimental results from both real-world and benchmark datasets demonstrate that MEFFGRN achieves competitive performance compared with state-of-the-art computational methods. Furthermore, study findings from SVCV-infected EPC cells suggest that MEFFGRN can predict novel gene regulatory relationships.

Extra Trees Method for Predicting LncRNA-Disease Association Based On Multi-Layer Graph Embedding Aggregation.

Lots of experimental studies have revealed the significant associations between lncRNAs and diseases. Identifying accurate associations will provide a new perspective for disease therapy. Calculation-based methods have been developed to solve these problems, but these methods have some limitations. In this paper, we proposed an accurate method, named MLGCNET, to discover potential lncRNA-disease associations. Firstly, we reconstructed similarity networks for both lncRNAs and diseases using top k similar information, and constructed a lncRNA-disease heterogeneous network (LDN). Then, we applied Multi-Layer Graph Convolutional Network on LDN to obtain latent feature representations of nodes. Finally, the Extra Trees was used to calculate the probability of association between disease and lncRNA. The results of extensive 5-fold cross-validation experiments show that MLGCNET has superior prediction performance compared to the state-of-the-art methods. Case studies confirm the performance of our model on specific diseases. All the experiment results prove the effectiveness and practicality of MLGCNET in predicting potential lncRNA-disease associations.

[Adrenal function evaluation using ACTH stimulation test in children with sepsis and septic shock].

OBJECTIVE: Sepsis and septic shock remain a common problem that results in significant mortality and morbidity in pediatric intensive care units (PICU). According to literature, the use of more physiologic steroid replacement therapy is associated with hemodynamic and survival benefits in adult patients with relative adrenal insufficiency (RAI) and catecholamine-resistant septic shock. But little information is available in children. The aim of the current prospective study was to determine the prevalence of adrenal insufficiency in children with sepsis and septic shock using a low-dose adrenocorticotropic hormone (ACTH) stimulation test (1 microg/1.73 m2) in children. METHODS: The authors performed cortisol estimation at baseline and after low-dose (1 microg/1.73 m2) ACTH stimulation at 30 mins in children during the first 24 hours in patients with sepsis or septic shock admitted to our PICU. Adrenal insufficiency was defined as a response < or = 90 microg/L. Absolute adrenal insufficiency (AAI) was further defined as baseline cortisol (T0) < 200 microg/L and RAI insufficiency by T0 > or = 200 microg/L. RESULTS: Sixty-two consecutive cases with sepsis and septic shock admitted to PICU of Shanghai Jiaotong University Affiliated Children's Hospital from April, 2006 to March, 2007. The median age was 37.6 months (range, 2 - 168 months), and their gender distribution was 42 (67.7%) males and 20 (32.3%) females, 53 cases had sepsis (85.5%) and 9 had septic shock (14.5%). The mean pediatric critical illness score (PCIS) was 79.3 +/- 9.2 and median pediatric risk of mortality score (PRMSIII) 11.3 (5 - 19), respectively. Overall mortality of sepsis and septic shock was 27.42%. The evaluation of adrenal insufficiency was conducted as follows. (1) The mean cortisol levels at baseline (T0) and 30 mins after ACTH stimulation (T1) were (318.6 +/- 230.4) microg/L, (452.3 +/- 230.7) microg/L and (454.7 +/- 212.7) microg/L, (579.3 +/- 231.9) microg/L in patients with severe sepsis and septic shock group, respectively. There were no significant difference between the two groups (P > 0.05). (2) The proportion of patients with adrenal insufficiency in the study population was 40.3% as defined by a response < or = 90 microg/L post test. The proportion of patients with adrenal insufficiency in sepsis and septic shock were 39.6% and 44.4%, respectively (chi2) = 0.073, P > 0.05). (3) The serum T0 and T1 levels were (320.5 +/- 223.9) microg/L, (462.3 +/- 212.0) microg/L and (384.3 +/- 258.3) microg/L, (500.7 +/- 470.6) microg/L, respectively, and the proportion of patients with adrenal insufficiency were 37.8% and 47.1% in the survivors and the dead (P > 0.05). The levels of T0 and T1 were related to the PCIS (P < 0.05). The morbidity of adrenal insufficiency was not related to the PCIS, PRISMIII, and number of organ that developed functional insufficiency (P > 0.05). CONCLUSIONS: Adrenal insufficiency may occur in patients with sepsis and septic shock in children. ACTH stimulation test may be helpful to determine whether corticosteroid therapy has a survival benefit in patients with relative adrenal insufficiency. A low-dose ACTH stimulation test can be used to evaluate the adrenal function status of severe sepsis and septic shock in children.