ChatGPT Meets Drug Discovery: Unlocking New Frontiers
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CASE STUDY
Cutting- Edge Cancer Tx Accelerated Target ID by 75% using ML & Curated Biomolecular Data
Key Highlights
The company aimed to find targets for differentiation therapy for cancers including AML.
Data retrieval, harmonization and ML model development are time-consuming.
To help the company circumvent the time factor, Elucidata curated an omics data atlas and developed custom pipelines.
The company was able to identify two targets in 2-3 months compared to the 1-2 years that it generally takes.
All Case Studies
Oncology Company Achieves ~80% Acceleration in Gene Target ID/ Validation
Pharma-AI Collaboration Drives ~$3M Cost Reduction by Using Highly Curated Public Data
Leading Oncology Company Uses Elucidata’s Drug OmixAtlas to Drive Drug Discovery
Polly PeakML Accelerates Biological Insight Derivation from Untargeted Metabolomics Analysis
Cutting- Edge Cancer Tx Accelerated Target ID by 75% using ML & Curated Biomolecular Data
Cloud Processing of Metabolomics Data
Accelerate Cell-Type Annotation of scRNA-seq Data
Predicting Survivability of Patients with Liver Hepatocellular Carcinoma
Integrated Metabolomics and Transcriptomics Profiling
Network Integration of Parallel Metabolic and Transcriptional Data Reveals Metabolic Modules that Regulate Macrophage Polarization
Why Polly CRISPR Screening is Better than MAGeCK VISPR
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