The large volume of such data posses challenges for data acquisition, data integration, multiple data modalities (either data model of storage model, storage, processing, and visualization. Biological experiments generate the prodigious amount of data in various formats(semi-structured or unstructured). In biological OMICS landscape, Interactomics is one of the new disciplines that focuses mainly on the data modeling, data storage, and retrieval of biological interaction data.
Examples of prominent graph databases are Neo4j, Titan, and OrientDB etc. Highly connected world, general purpose graph databases are providing opportunities to experience benefits of semantically significant networks without investing on the graph infrastructure. Graph is an expressive way to represent dynamic and complex relationships in highly connected data. Experimental results reveal that the proposed graph-based deep learning approach outperforms the Random Forests (RsF) and two deep learning models, i.e., CNN and ANN under different combinations of graph features. The proposed GCN approach is further augmented with the shortest path and eigenvector centrality attribute to significantly improve the accuracy of sales prediction. We evaluate the performance of the proposed GCN model on graph databases and compare it with Random Forest (RF), Convolutional Neural Network (CNN), and Artificial Neural Network (ANN). This is the first step towards a GCN-based binary classification based on graph databases in the domain of B2B CRM. Specifically, we have applied the graph convolution network (GCN) to enable CRM analytics to forecast sales. Secondly, the graph database has been investigated by using data mining and exploratory analysis coupled with cypher graph query language. In our approach, in the first instance, we have designed a graph database using the Neo4j platform. This paper introduces a graph-based analytics approach to improve CRM within a B2B environment. In a business-to-business (B2B) customer relationship management (CRM) use case, each client is a potential business organization/company with a solid business strategy and focused and rational decisions. Information which is collected from major heterogeneousīiological data repositories, by using graph database. This paper aims atĭesigning a well suited graphical data storage model for biological Model, storage, processing and visualization. Integration, multiple data modalities (either data model of storage Volume of such data posses challenges for data acquisition, data
In various formats(semi-structured or unstructured). Biological experiments generate prodigious amount of data Modeling, data storage and retrieval of biological interactionĭata.
Is one of the new disciplines that focuses mainly on the data In biological OMICS landscape, Interactomics Significant networks without investing on the graph infrastructure.Įxamples of prominent graph databases are: Neo4j, TitanĪnd OrientDB etc. Providing opportunities to experience benefits of semantically Highly connected world, general purpose graph databases are Graph is an expressive way to represent dynamicĪnd complex relationships in highly connected data.
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