The proposed approach is largely motivated by the issues related to querying datasets by clinical researchers, governmental or non-governmental organizations in monitoring health care services to improve quality of care.
In addition, we demonstrate that our extracted seed-network of correlated mouse genes is an effective tool for querying datasets and provides a context to generate hypotheses.
Using data providers to query datasets is done through a RESTful API.
SPARQL is the W3C [Prud and Seaborne, 2006] recommendation for querying datasets structured according to the Resource Description Framework (RDF) [Lassila and Swick, 1998].
They queried the dataset for instances where a tissue is associated with the gene TSPAN6 which expresses a medium and a low expression levels.
Results also show that performance of StreamAligner gets better in comparison to SparkBWA as the size of query dataset increases.
Further, we introduce a novel cache replacement strategy, fine-tuned to the described query dataset, and include explicit support for the Open World Assumption.
The trained classifier can be used to distinguish true positive PPI pairs from the noisy query dataset based on the values of genomic evidences of each pair.
The query dataset used for the analysis is the proteome of M. tuberculosis H37Rv.
