Cancer Therapy Breakthrough: Elucidata Helps Identify Novel Target, Advances to Trials, Saves $236M
Key Highlights
In collaboration with Elucidata, a US-based therapeutics company identified a novel Acute Myeloid Leukemia target in just 6 months. It has advanced to clinical trials, offering hope to 100k+ patients.
In their pursuit of a target for cancer differentiation therapy, they faced challenges such as a lack of expertise and resources to gather and integrate diverse data, establish a standard multi-omics pipeline for target identification, and construct custommachine learning models.
Elucidata helped the therapeutics company by harmonizing ~10,000 multi-modal datasets to identify differentiation targets and to predict the ideal patient sub-population (potential responders and non-responders) using customized classifier models.
This high-quality data and customized solutions allowed the therapeutic company to achieve a 4X acceleration in target ID and validation, saving approximately $236M through efficient and expedited data reuse.
Cancer Therapy Breakthrough: Elucidata Helps Identify Novel Target, Advances to Trials, Saves $236M
Key Highlights
In collaboration with Elucidata, a US-based therapeutics company identified a novel Acute Myeloid Leukemia target in just 6 months. It has advanced to clinical trials, offering hope to 100k+ patients.
In their pursuit of a target for cancer differentiation therapy, they faced challenges such as a lack of expertise and resources to gather and integrate diverse data, establish a standard multi-omics pipeline for target identification, and construct custommachine learning models.
Elucidata helped the therapeutics company by harmonizing ~10,000 multi-modal datasets to identify differentiation targets and to predict the ideal patient sub-population (potential responders and non-responders) using customized classifier models.
This high-quality data and customized solutions allowed the therapeutic company to achieve a 4X acceleration in target ID and validation, saving approximately $236M through efficient and expedited data reuse.
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