Designed to help humans understand data, to advance ongoing scientific studies and to establish the foundation for future science-led innovation.
Life sciences data is messy, noisy and usually incomplete, requiring specialized interpretation skills, involving highly-skilled individuals in laborious, error-prone data wrangling.
Exponential growth in biological data available for scientific exploration outstrips the capability of humans to interpret and generate meaningful insight.
e[datascientist] exploration applications provide a scalable, industry-defining alternative to address these current challenges. These provide a step-change in accessibility and understanding of the complexities of multi-dimensional data for every scientist.
Underpinned by a reliable, trusted data fabric, the exploration applications enable the user to summarize, aggregate and graphically represent multi-omics data in a multi-layer hypergraph, and select the right method to address different scientific enquiries.
Contextualize data and navigate scientific data-entity relationships
e[exploratory data analysis]
Characterize data for statistical analysis
Generate, formulate and test novel hypotheses