Leading Oncology Company Uses Elucidata’s Drug OmixAtlas to Drive Drug Discovery
Key Highlights
The oncology company works on developing next-generation cancer therapies to overcome tumor resistance and improve outcomes for patients with cancer.
They generate large volumes of drug screening data from high throughput experiments to evaluate the effect of compounds on various cell lines and the health of cells.
Their major requirements were an automated pipeline for data ingestion, a platform to store multiple data types associated with an experiment in a structured format, and an interactive dashboard to generate custom reports.
Our Drug OmixAtlas could effectively cater to all their needs by automating the ingestion process, standardizing the data with uniform ontologies, and harmonizing metadata to make any data findable from ongoing/ historical records.
This collaboration resulted in an efficient, scalable process with a 25X reduction in the time taken for a single analysis. We also contributed to making their data FAIR- Findable, Accessible, Interoperable, and Reusable.
Read the case study to know more and find out what their scientist had to say about the collaboration.
Leading Oncology Company Uses Elucidata’s Drug OmixAtlas to Drive Drug Discovery
Key Highlights
The oncology company works on developing next-generation cancer therapies to overcome tumor resistance and improve outcomes for patients with cancer.
They generate large volumes of drug screening data from high throughput experiments to evaluate the effect of compounds on various cell lines and the health of cells.
Their major requirements were an automated pipeline for data ingestion, a platform to store multiple data types associated with an experiment in a structured format, and an interactive dashboard to generate custom reports.
Our Drug OmixAtlas could effectively cater to all their needs by automating the ingestion process, standardizing the data with uniform ontologies, and harmonizing metadata to make any data findable from ongoing/ historical records.
This collaboration resulted in an efficient, scalable process with a 25X reduction in the time taken for a single analysis. We also contributed to making their data FAIR- Findable, Accessible, Interoperable, and Reusable.
Read the case study to know more and find out what their scientist had to say about the collaboration.
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