Despite major advances in technology, the mantra ‘garbage in, garbage out’ is still pivotal to any computational approach. The accuracy and reliability of new technologies, such as machine learning and artificial intelligence, remain critically dependent on the quality of the data used as an input. Ultimately, the presence of messy data hampers both data integration and the ability to extract insight from it.
SciBite delivers the unique combination of comprehensive hand-curated ontologies, user friendly data cleansing tools and unparalleled expertise in biocuration. We transform your messy data into the clean, high quality, contextualised data necessary for downstream integration and discovery activities to be effective.
“Cleaning Big Data: Most Time-Consuming, Least Enjoyable Data Science Task, Survey Says” Forbes, 23rd March 2016
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For most pharmaceutical companies, extracting insight from heterogeneous and ambiguous data remains a challenge. The era of data-driven R&D is motivating investment in technologies such as machine learning to provide deeper insights into new drug development strategies.
The quality of data directly impacts the accuracy and reliability of results of computational approaches. However, the work required to achieve clean, high quality data can be costly, often prohibitively so, requiring data scientists to spend the majority of their time as ‘data janitors’, rather than actually analysing data.
SciBite provides an integrated, cost-effective solution to significantly reduce the time and cost associated with the process of data cleansing, normalisation and annotation. The output ensures that downstream integration and discovery activities are based on high quality, contextualised data.
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