A novel subnetwork representation learning method for uncovering disease-disease relationships.

Analyzing disease-disease relationships plays an important role for understanding disease mechanisms and finding alternative uses for a drug. A disease is usually the result of abnormal state of multiple molecular process. Since biological networks can model the interplay of multiple molecular processes, network-based methods have been proposed to uncover the disease-disease relationships recently. Given a disease and a network, the disease could be represented as a subnetwork constructed by the disease genes involved in the given network, named disease subnetwork. Because it is difficult to learn the feature representation of disease subnetworks, most existing methods are unsupervised ones without using labeled information. To fill this gap, we propose a novel method named SubNet2vec to learn the feature vectors of diseases from their corresponding subnetwork in the biological network. By utilizing the feature representation of disease subnetwork, we can analyze disease-disease relationships in a supervised fashion. The evaluation results show that the proposed framework outperforms some state-of-the-art approaches in a large margin on disease-disease/disease-drug association prediction. The source code and data are available athttps://github.com/MedicineBiology-AI/SubNet2vec.git.

A learning-based framework for miRNA-disease association identification using neural networks.

MOTIVATION: A microRNA (miRNA) is a type of non-coding RNA, which plays important roles in many biological processes. Lots of studies have shown that miRNAs are implicated in human diseases, indicating that miRNAs might be potential biomarkers for various types of diseases. Therefore, it is important to reveal the relationships between miRNAs and diseases/phenotypes. RESULTS: We propose a novel learning-based framework, MDA-CNN, for miRNA-disease association identification. The model first captures interaction features between diseases and miRNAs based on a three-layer network including disease similarity network, miRNA similarity network and protein-protein interaction network. Then, it employs an auto-encoder to identify the essential feature combination for each pair of miRNA and disease automatically. Finally, taking the reduced feature representation as input, it uses a convolutional neural network to predict the final label. The evaluation results show that the proposed framework outperforms some state-of-the-art approaches in a large margin on both tasks of miRNA-disease association prediction and miRNA-phenotype association prediction. AVAILABILITY AND IMPLEMENTATION: The source code and data are available at https://github.com/Issingjessica/MDA-CNN. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Measuring phenotype-phenotype similarity through the interactome.

BACKGROUND: Recently, measuring phenotype similarity began to play an important role in disease diagnosis. Researchers have begun to pay attention to develop phenotype similarity measurement. However, existing methods ignore the interactions between phenotype-associated proteins, which may lead to inaccurate phenotype similarity. RESULTS: We proposed a network-based method PhenoNet to calculate the similarity between phenotypes. We localized phenotypes in the network and calculated the similarity between phenotype-associated modules by modeling both the inter- and intra-similarity. CONCLUSIONS: PhenoNet was evaluated on two independent evaluation datasets: gene ontology and gene expression data. The result shows that PhenoNet performs better than the state-of-art methods on all evaluation tests.

An online tool for measuring and visualizing phenotype similarities using HPO.

BACKGROUND: The Human Phenotype Ontology (HPO) is one of the most popular bioinformatics resources. Recently, HPO-based phenotype semantic similarity has been effectively applied to model patient phenotype data. However, the existing tools are revised based on the Gene Ontology (GO)-based term similarity. The design of the models are not optimized for the unique features of HPO. In addition, existing tools only allow HPO terms as input and only provide pure text-based outputs. RESULTS: We present PhenoSimWeb, a web application that allows researchers to measure HPO-based phenotype semantic similarities using four approaches borrowed from GO-based similarity measurements. Besides, we provide a approach considering the unique properties of HPO. And, PhenoSimWeb allows text that describes phenotypes as input, since clinical phenotype data is always in text. PhenoSimWeb also provides a graphic visualization interface to visualize the resulting phenotype network. CONCLUSIONS: PhenoSimWeb is an easy-to-use and functional online application. Researchers can use it to calculate phenotype similarity conveniently, predict phenotype associated genes or diseases, and visualize the network of phenotype interactions. PhenoSimWeb is available at http://120.77.47.2:8080.

Identifying term relations cross different gene ontology categories.

BACKGROUND: The Gene Ontology (GO) is a community-based bioinformatics resource that employs ontologies to represent biological knowledge and describes information about gene and gene product function. GO includes three independent categories: molecular function, biological process and cellular component. For better biological reasoning, identifying the biological relationships between terms in different categories are important. However, the existing measurements to calculate similarity between terms in different categories are either developed by using the GO data only or only take part of combined gene co-function network information. RESULTS: We propose an iterative ranking-based method called C r o G O2 to measure the cross-categories GO term similarities by incorporating level information of GO terms with both direct and indirect interactions in the gene co-function network. CONCLUSIONS: The evaluation test shows that C r o G O2 performs better than the existing methods. A genome-specific term association network for yeast is also generated by connecting terms with the high confidence score. The linkages in the term association network could be supported by the literature. Given a gene set, the related terms identified by using the association network have overlap with the related terms identified by GO enrichment analysis.

[Establishment of a stable HEK293T cell line with c.392G>T (p.131G>V) mutation site knockout in G6PD gene using CRISPR/Cas9 technique].

OBJECTIVE: To establish a stable HEK293T cell line with c.392G>T (p.131G>V) mutation site knockout in G6PD gene using CRISPR/Cas9 technique. METHODS: We designed 4 pairs of small guide RNA (sgRNA) for G6PD c.392G>T(p.131G>V) mutation site, and constructed exogenous PX458 plasmids expressing Cas9-sgRNA. The plasmids were transfected into HEK293T cells, and the cells expressing GFP fluorescent protein were separated by flow cytometry for further culture. After verification of the knockout efficiency using T7 endonuclease Ⅰ, the monoclonal cells were screened by limiting dilution and DNA sequencing to confirm the knockout. We detected the expressions of G6PD mRNA and protein and examined functional changes of the genetically modified cells. RESULTS: We successfully constructed the Cas9-sgRNA exogenous PX458 plasmid based on the c.392G>T(p.131G>V) mutation site of G6PD gene. The editing efficiency of the 4 pairs of sgRNA, as detected by T7E1 enzyme digestion, was 6.74%, 12.36%, 12.54% and 2.94%. Sanger sequencing confirmed that the HEK293T cell line with stable knockout of G6PD c.392G>T(p.131G>V) was successfully constructed. The genetically modified cells expressed lower levels of G6PD mRNA and G6PD protein and showed reduced G6PD enzyme activity and proliferative capacity and increased apoptosis in response to vitamin K3 treatment. CONCLUSIONS: We successfully constructed a stable HEK293T cell model with G6PD gene c.392G>T(p.131G>V) mutation site knockout to facilitate future study of gene repair.

Improving the measurement of semantic similarity by combining gene ontology and co-functional network: a random walk based approach.

BACKGROUND: Gene Ontology (GO) is one of the most popular bioinformatics resources. In the past decade, Gene Ontology-based gene semantic similarity has been effectively used to model gene-to-gene interactions in multiple research areas. However, most existing semantic similarity approaches rely only on GO annotations and structure, or incorporate only local interactions in the co-functional network. This may lead to inaccurate GO-based similarity resulting from the incomplete GO topology structure and gene annotations. RESULTS: We present NETSIM2, a new network-based method that allows researchers to measure GO-based gene functional similarities by considering the global structure of the co-functional network with a random walk with restart (RWR)-based method, and by selecting the significant term pairs to decrease the noise information. Based on the EC number (Enzyme Commission)-based groups of yeast and Arabidopsis, evaluation test shows that NETSIM2 can enhance the accuracy of Gene Ontology-based gene functional similarity. CONCLUSIONS: Using NETSIM2 as an example, we found that the accuracy of semantic similarities can be significantly improved after effectively incorporating the global gene-to-gene interactions in the co-functional network, especially on the species that gene annotations in GO are far from complete.