CYTOSCAPE MULTIPLE EDGES HOW TO

CYTOSCAPE MULTIPLE EDGES HOW TO

In this paper, we describe how to use the Neo4J graph database to integrate a variety of types of data sets in the context of systems vaccinology. While many existing biological databases are implemented using the traditional SQL (Structured Query Language) database technology, NoSQL database technologies have been explored as a more relationship-based, flexible and scalable method of data integration. Systems biology involves the integration of multiple data types (across different data sources) to offer a more complete picture of the biological system being studied. Querying a graph database through such relationships has the potential to discover novel relationships among heterogeneous biological data and metadata. We have demonstrated the feasibility and flexibility of using a graph database for storing and querying immunological data with complex biological relationships. Finally, we have built a prototype to allow the user to query the graph database in natural language. We have also implemented a visual graph query interface for users to browse graph data. In addition to using a graph-based query language (Cypher) for data retrieval, we have developed a web-based dashboard that allows users to easily browse and plot data without the need to learn Cypher. We have created a graph database integrating data from multiple sources. While SQL (Structured Query Language) databases are popular in the biomedical domain, NoSQL database technologies have been used as a more relationship-based, flexible and scalable method of data integration. Many systems biology studies leverage the integration of multiple data types (across different data sources) to offer a more comprehensive view of the biological system being studied. However, the result raised questions as to whether the medical resources were being diverted to less serious conditions than more urgent conditions in patients with SMIs. Physical comorbidities in patients with mental illnesses were more extensive than those with other disease categories. However, implicit associations between metabolic syndrome and SMIs were suggested in comorbidity networks. Unexpectedly, the diagnosis of metabolic syndrome was only scarcely found in patients with severe mental illnesses (SMIs). The most noticeable associations were 1) neurotic illnesses with gastrointestinal/pain disorders and 2) dementia with fracture, Parkinson's disease, and cerebrovascular accidents. The number of physical diagnoses among them was 11.2, which was 1.6 times higher than non-psychiatric groups. In 1,017,024 individuals, 77,447 (7.6%) were diagnosed with mental illnesses. From the patient-diagnosis data, a network of diagnoses was constructed using Jaccard coefficient as the index of comorbidity. For every combination of psychiatric and physical diagnoses, odds ratios were calculated adjusting age and sex. We investigated this complexity by conventional logistic regression and network analysis.Ī health insurance claims data in Korea was analyzed. The nature of physical comorbidities in patients with mental illness may differ according to diagnosis and personal characteristics.

Cytoscape multiple edges