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dc.contributor.authorPatton, Evan
dc.contributor.authorBrown, Elisabeth
dc.contributor.authorPoegel, Matt
dc.contributor.authorDe Los Santos, Hannah
dc.contributor.authorFasano, Christopher
dc.contributor.authorBennett, Kristin
dc.contributor.authorMcGuinness, Deborah
dc.date.accessioned2022-02-18T02:34:27Z
dc.date.available2022-02-18T02:34:27Z
dc.date.issued2015-10-11
dc.identifier.other74
dc.identifier.urihttp://archive.tw.rpi.edu/media/latest/SemStats2015-final.pdf
dc.identifier.urihttps://hdl.handle.net/20.500.13015/4476
dc.description.abstractCombining statistical techniques with semantic data representations holds the potential to enhance understandability of scientific results. It can augment scientific findings with existing data sources in a reproducible manner through provenance capture, as well as enable further analysis and deduction through computer and human understandable definitions of terms. We present a framework for semantically integrating and exploring numerical analyses. We call our work SemNExT for Semantic Numeric Exploration Technology. We apply our approach to data analysis aimed at improving understanding of human brain development that leverages the Cortecon RNA-Seq data repository. Our approach supports enrichment of Cortecon data through combinations with structured data sources available via SQL or SPARQL from the web to provide semantically enhanced analyses combined with statistical analyses. Our results are encoded as RDF graphs that may be used as input to reasoners and may drive provenance-aware visualizations. We introduce our infrastructure, describe its use on transcriptomic data analysis of a model of cerebral cortex development, and discuss some emerging suggestions for best practices and future research challenges.
dc.relation.urihttps://tw.rpi.edu/project/SemNExT
dc.subjectSemantic Numeric Exploration Technology (SemNExT)
dc.titleSemNExT: A Framework for Semantically Integrating and Exploring Numeric Analyses


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